MONITORING AND MAPPING ASIAN CITRUS PSYLLID USING … · monitoring and mapping asian citrus...

MONITORING AND MAPPING ASIAN CITRUS PSYLLID USING SHAKING MACHINE

By

MUNA JAMIL ABBAS

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2017

© 2017 Muna Jamil Abbas

To my husband and my parents

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ACKNOWLEDGMENTS

A sincere praise to the Allah, who illuminated the path for me, opened the doors

of knowledge and gave me the patience to complete my graduate studies and research

work. I would like to thank my advisor, Dr. Reza Ehsani, professor of Agricultural and

Biological Engineering, University of Florida for the opportunity of pursuing a doctoral

degree and for his continuous support during my doctoral study.

Also, I would like to thank my supervisory committee members, Dr. Ray Bucklin,

professor of Agricultural and Biological Engineering, University of Florida, Dr. Won Suk

“Daniel” Lee, professor of Agricultural and Biological Engineering, University of Florida,

Dr. John K. Schueller, professor of the Mechanical and Aerospace Engineering,

Dr. Kirsten Plez-Stelinski, associate professor of Entomology Department, University of

Florida for their advice, guidance during doctoral study.

I would like to thank all the staff, colleagues in Dr. Ehsaniʼs lab in Citrus

Research and Education Center in Lake Alfred, University of Florida for their help and

support.

I would like to thank the faculty, staff, and colleagues in Entomology and

Nematology Department in Citrus Research and Education Center in Lake Alfred,

University of Florida for their help and support.

I am grateful to my dear husband Salah and my lovely daughters, Iyat and

Shahad, who are the all of my life. My husband has sacrificed a lot to take care of my

daughters to let me concentrate on my studying and research. I would like to express

my gratitude to him.

My deepest words of gratitude go to my family in Iraq who always pray for my

success.

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Finally, I would like to thank the Iraqi ministry of higher education for giving me

the opportunity to attend University of Florida to pursue a doctoral degree and for their

financial support.

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TABLE OF CONTENTS

Page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF TABLES ............................................................................................................ 9

LIST OF FIGURES ........................................................................................................ 10

LIST OF ABBRIVATION ................................................................................................ 14

ABSTRACT ................................................................................................................... 15

CHAPTER

1 INTRODUCTION .................................................................................................... 17

Problem Statement ................................................................................................. 19 Specific Objectives ................................................................................................. 19

2 LITRATURE REVEIW ............................................................................................. 20

Identification of Asian Citrus Psyllid (ACP) ............................................................. 20 Ecology of Asian Citrus Psyllid ............................................................................... 21

Monitoring Methods for Asian Citrus Psyllid ............................................................ 22 Visual Sampling ................................................................................................ 23

Tap Sampling ................................................................................................... 23 Sticky Traps ...................................................................................................... 24

Sweep Nets ...................................................................................................... 27 D Vacuum ......................................................................................................... 28

Suction Traps ................................................................................................... 28 Models Proposed to Predict Insect Variation over an Area ..................................... 28

Leaf Washing Method ...................................................................................... 28 A Shake - and – Washing Technique ............................................................... 29

Two Simple Insect Sampling Devices .............................................................. 30 Washing Machine for Arthropods Recovery ..................................................... 30 Three Different Vacuum Devices for ACP Detection ........................................ 31 An Automated System for Detection and Extraction Pest in Paddy Field ......... 32

An Autonomous System for Insect Pest Detection ........................................... 33 Shaker Machine for Different Applications .............................................................. 33

Mobile Limb Shaker .......................................................................................... 33

Trunk Shaker .................................................................................................... 34 Vertical Canopy Shaker .................................................................................... 35

3 EVALUATION OF SAMPLING PATTERNS METHODS TO DETECT AND MONITOR ASIAN CITRUS PSYLLID IN CITRUS GROVES .................................. 37

Materials and Methods............................................................................................ 38

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The Study Area ................................................................................................ 38

Insect Sampling Procedures: ............................................................................ 38 Results and Discussion........................................................................................... 43

Statistical Analysis ............................................................................................ 43 Exploratory Statistics for Asian Citrus Psyllid under Different Sampling

Patterns with Different Traps Position ........................................................... 44 Comparison of the Interpolated Maps by the Two Sampling Patterns and

Two Different Traps Position ......................................................................... 46

Measures of Accuracy and Effectiveness of Prediction Maps .......................... 54

4 EVALUATION OF MEASURED ACCELERATION PRODUCED BY THE CONVENTIONALTAP METHOD AND A DEVELOPED SHAKING MACHINE FOR ASIAN CITRUS PSYLLID SAMPLING ........................................................... 56

Materials and Methods............................................................................................ 56 Tap Method Experiment ................................................................................... 56

Tap Method Experiment ................................................................................... 57 Accelerometer .................................................................................................. 60

Determination of Tree Limb Parameters........................................................... 60 Data Extraction ................................................................................................. 60 Shaking Machine Experiment ........................................................................... 63

Development of Limb Shaker ........................................................................... 63 Design and Construction .................................................................................. 63

Field Experiment .............................................................................................. 72 Data Extraction ................................................................................................. 73

Results and Discussion........................................................................................... 75 Tap Method Experiment Results ...................................................................... 75 Shaking Machine Experiment Results .............................................................. 76

5 UTILIZING A DEVELOPED SHAKING MACHINE AND DIFFERENT INTERPOLATION TECHNIQUES FOR MONITORING AND MAPPING ASIAN CITRUS PSYLLID ................................................................................................... 80

Material and Methods ............................................................................................. 82 Study Area ........................................................................................................ 82

Insect Sampling Procedure and Data Collection .............................................. 83 Zigzag Pattern Sampling: ........................................................................... 83

Sampling Technique .................................................................................. 84 Recognizing Psyllids on Sticky Traps ............................................................... 85

Geostatistical Method for Interpolating Asian Citrus Psyllid Distribution. .......... 86 Result and Discussion ............................................................................................ 87

Exploratory Statistics for Asian Citrus Psyllid under Different Sampling Patterns with Different Traps Position ........................................................... 87

Prediction of Asian Citrus Psyllid Distribution. .................................................. 87

Surface Mapping .............................................................................................. 88 Measures of Accuracy of Prediction Maps ....................................................... 88

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6 ASIAN CITRUS PSYLLID MONITORING CALCULATIONS .................................. 94

Field Capacity and Efficiency .................................................................................. 94 Comparison of the Labor Cost Using the Conventional Tap Sampling Method

Versus the Shaking Machine Method .................................................................. 95

7 CONCLUSIONS AND FUTURE WORK ................................................................. 98

Conclusions ............................................................................................................ 98 Future Work ............................................................................................................ 99

APPENDIX: MATLAB CODES .................................................................................... 101

LIST OF REFERENCES ............................................................................................. 105

BIOGRAPHICAL SKETCH .......................................................................................... 110

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LIST OF TABLES

Table page 3-1 Analysis of variance of data on the number of psyllids captured at different

trap position (horizontal and vertical). ................................................................. 43

3-2 Descriptive statistics for ACP distribution using different sampling methods ...... 44

3-3 The means of mean (M) and root mean square error (RMSE) for different traps placement under two different sampling patterns. ..................................... 45

3-4 Accuracy and effectiveness measurements for the sampling patterns methods by using different interpolation methods. ............................................. 54

4-1 Setup of the tap method experiment ................................................................... 59

4-2 Accelerometer parameters ................................................................................. 60

4-3 Tree limb parameters ......................................................................................... 60

4-4 Parameters of the limb and sensor location for the shaking machine experiment .......................................................................................................... 73

4-5 Setup of the shaking machine method experiment ............................................. 73

4-6 The output of the ANOVA analysis for shaking time and limb length using the tap method.......................................................................................................... 76

4-7 The output of the ANOVA analysis for shaking duration and shaking frequency using the shaking machine. ............................................................... 79

5-1 Descriptive statistics for ACP distribution using zigzag sampling pattern. .......... 87

5-2 The means of mean (M) and root mean square error (RMSE) for different interpolation methods. ........................................................................................ 91

5-3 RMSE, Error (%) and RMSE for different interpolation methods. ....................... 91

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LIST OF FIGURES

Figure page 1-1 Citrus greening (HLB) disease symptoms .......................................................... 18

2-1 Asian Citrus Psyllid and its three different stages ............................................... 20

2-2 Visual inspecting for the new flush ..................................................................... 23

2-3 Use of clipboard (left) and number of psyllids on white clipboard (right). ............ 24

2-4 Use the sticky traps (left) and number of Asian citrus psyllids on the traps (right) .................................................................................................................. 25

2-5 Using the sweep net for psyllid detection. .......................................................... 27

2-6 Leaf washing method filtering device. ................................................................. 29

2-7 Leaf washing machine for Arthropods recovery from plant leaves. .................... 31

2-8 Vacuum devices used for detect Asian citrus psyllid. ......................................... 32

2-9 Global architectural design. ................................................................................ 32

2-10 Trap design......................................................................................................... 33

2-11 Mobile limb shaker for apple harvester. .............................................................. 34

2-12 Olive trunk shaker ............................................................................................... 35

2-13 Shaker unit with crank drive system before adding flywheel. ............................. 36

3-1 Study area. ......................................................................................................... 38

3-2 Two different patterns of sticky traps distribution used to monitor psyllids population. .......................................................................................................... 41

3-3 Two positions for traps' placement. .................................................................... 42

3-4 Distribution of sample points representing the location of Asian Citrus Psyllid monitoring reference points. ............................................................................... 42

3-5 The plot of number of psyllids captured by different traps position ..................... 44

3-6 The cross validation comparison of the ACP distribution map by different interpolation methods using grid pattern. ............................................................ 46

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3-7 The cross validation comparison of the ACP distribution map by different interpolation methods using zigzag pattern. ....................................................... 46

3-8 Distribution of Asian citrus psyllid under grid pattern with horizontal traps position using Inverse distance weighting method. ............................................. 48

3-9 Distribution of Asian citrus psyllid under grid pattern with horizontal traps position using ordinary kriging method. .............................................................. 48

3-10 Distribution of Asian citrus psyllid under grid pattern with horizontal traps position using simple kriging method. ................................................................. 49

3-11 Distribution of Asian citrus psyllid under grid pattern with vertical traps position using Inverse distance weighting method. ............................................. 49

3-12 Distribution of Asian citrus psyllid under grid pattern with vertical traps position using ordinary kriging method. .............................................................. 50

3-13 Distribution of Asian citrus psyllid under grid pattern with vertical traps position using simple kriging method. ................................................................. 50

3-14 Distribution of Asian citrus psyllid under zigzag pattern with horizontal traps position using inverse distance weighting method. ............................................. 51

3-15 Distribution of Asian citrus psyllid under zigzag pattern with horizontal traps position using ordinary kriging method. .............................................................. 51

3-16 Distribution of Asian citrus psyllid under zigzag pattern with horizontal traps position using simple kriging method. ................................................................. 52

3-17 Distribution of Asian citrus psyllid under zigzag pattern with vertical traps position using inverse distance weighting method. ............................................. 52

3-18 Distribution of Asian citrus psyllid under zigzag pattern with vertical traps position using ordinary kriging method. .............................................................. 53

3-19 Distribution of Asian citrus psyllid under zigzag pattern with vertical traps position simple kriging method. .......................................................................... 53

4-1 Study area. ......................................................................................................... 57

4-2 Rod used for shaking the limbs. ......................................................................... 58

4-3 Experimental setup. ............................................................................................ 58

4-4 Circuit diagram for connecting accelerometer sensors to Arduino UNO R3 and connecting Arduino UNO R3 to a laptop via a USB cable. .......................... 59

4-5 Acceleration wavelength ..................................................................................... 62

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4-6 Statistical analysis peaks for resultant acceleration including: Histogram, Boxplot, and normal distribution. ........................................................................ 63

4-7 The front frame of the tractor attached to the main frame of the shaking machine. ............................................................................................................. 65

4-8 The components of the hydraulic system of the shaking machine:..................... 66

4-9 Top view of the transmission system of the shaking machine. ........................... 67

4-10 Top view schematic of the transmission system and the shaking system .......... 68

4-11 Rod used for shaking the limbs. ......................................................................... 68

4-12 Components of shaking system. ......................................................................... 69

4-13 Clipboard tool and its dimensions. ...................................................................... 70

4-14 Side view of the shaking machine showing the shaking arm. ............................. 71

4-15 Components of the shaking machine. ................................................................ 71

4-16 Experiment Setup ............................................................................................... 73

4-17 Statistical analysis peaks including: Histogram, Boxplot, and normal distribution. ......................................................................................................... 74

4-18 Acceleration wavelength ..................................................................................... 75

4-19 The plot of acceleration for each combination of groups of shaking time and limb length. ......................................................................................................... 77

4-20 The plot acceleration for each combination of groups of shaking time and shaking frequency. ............................................................................................. 78

5-1 Study area. ......................................................................................................... 82

5-2 Distribution of sample points representing the location of Asian Citrus Psyllid monitoring points. ............................................................................................... 83

5-3 Developed shaking machine used for collecting Asian citrus psyllid in the field. .................................................................................................................... 84

5-4 Yellow sticky trap used for capturing Asian citrus psyllid. ................................... 85

5-5 Prediction map of Asian citrus psyllids using inverse distance weighting technique. ........................................................................................................... 89

5-6 Prediction map of Asian citrus psyllids using ordinary kriging technique. ........... 89

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5-7 Prediction map of Asian citrus psyllids using simple kriging technique. .............. 90

5-8 The cross validation comparison of the ACP distribution map between inverse distance weighting and simple kriging method. ...................................... 91

5-9 The cross validation comparison of the ACP distribution map between inverse weighting distance and ordinary kriging. ................................................ 92

5-10 The percent error with different interpolation methods for ACP prediction. ........ 92

6-1 Comparison of two different methods on the total time required to monitor Asian citrus psyllid in citrus groves. .................................................................... 97

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LIST OF ABBRIVATION

ACP

APHIS

CREC

ESRI

FASS

HLB

GPS

Asian Citrus Psyllid

Animal and Plant Health

Citrus Research and Education Center

Environmental Systems Research Institute

Florida Agricultural Statistics Service

Huanglongbing

Global Positioning System

GLM General Linear Model

IDW Inverse Distance Weighting

NASS National Agricultural Statistics Service

OK Ordinary Kriging

SK Simple kriging

PE Percent Error

RMSE Root-Mean-Squared –Error

RI Relative Improvement

USDA United States Department of Agriculture

USB Universal Serial Bus

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

MONITORING AND MAPPING ASIAN CITRUS PSYLLID USING SHAKING MACHINE

By

Muna Jamil Abbas

May 2017 Chair: Reza Ehsani Major: Agricultural and Biological Engineering

Asian citrus psyllid (ACP, Diaphorina citri Kuwayama) is a major pest of citrus in

Florida. ACPs are of particular importance because they spread citrus greening disease

or citrus Huanglongbing (HLB). HLB is one of the most serious problems affecting

Florida’s citrus industry due to rapid transmission of the disease within groves and

corresponding rapid decline in productivity of infected trees. The existing scouting

methods of manual insect pest sampling is time consuming, labor intensive and costly.

The accurate and rapid identification as well as ACP monitoring is highly recommended.

Therefore, the main goal of this research was to develop a machine for quick and

cost - effective collection of psyllids in citrus groves that simulates the tap sampling

method. A shaking machine was built and fabricated at the Citrus Research and

Education Center, Lake Alfred, FL. First, sampling techniques were tested in terms of

optimal placement (horizontal and vertical) of colored sticky traps that monitor ACP

density distribution using different sampling patterns (grid and zigzag). From the ACP

distribution information, geo-insect’s prediction maps were generated. The results

showed that there were non-significant differences between the trap positions at 0.05

level of significance. The spatial distribution for ACP created using three different

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interpolation methods: inverse distance weighting (IDW), ordinary kriging (OK), and

simple kriging (SK).

In order to define the shaking machine operation parameters, a second field test

was performed in the citrus groves. In this test, two different experiments were

performed. The first experiment was the tap sampling experiment. In this experiment a

modified version of the tap sampling method of Stansly et al., 2010 was performed, with

the exception that no insects were collected. The shaking duration (5, 10, and 15 s) and

the limb length (0.889, 0990, and 1.143 m) were selected as two important parameters

by which to define the acceleration exerted on the limb using the tap method. The

results show that there were no significant differences among the shaking duration and

limb length at 0.05 significance level. The second experiment was the shaking machine

experiment in which the shaking frequencies (0.26, 2.33, and 4.00 Hz) and shaking

durations (5, 10, and 15 sec) are two important parameters to define the acceleration

exerted on the limb using the shaking machine. The results showed that there was a

significant difference among the shaking frequencies and shaking duration with 0.05

significance level.

For monitoring and mapping the ACP, a fourth field test was performed in citrus

groves that simulate tap sampling method. In this field test, a shaking machine was

used to collect the psyllids in defined locations to follow the zigzag pattern. The ultimate

goal for this study is to utilize the shaking machine to monitor the ACP in citrus groves

and evaluate the accuracies of the generated maps under different interpolation

methods.

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CHAPTER 1 INTRODUCTION

The State of Florida accounted for 56% of the citrus production total of the United

States and California is 41% and 3% for Arizona and Texas (USDA, 2016).This makes

Florida the second largest producer of citrus in the world behind Brazil (Hodges and

Spreen, 2012).

Citrus could be attacked by various pests such as insects, mites, nematodes

which effect on the quality and quantity of the citrus yield. Plants can be attacked by

wide varieties of insects which can cause physical damages or carry a pathogen from

infected to healthy plants (Meyer, 2007). Asian citrus psyllid Diaphorina Citri Kuwayama

started in Asia, India and in the United States, it was first observed in Florida and Texas

in 1998 and 2001 respectively (Grafton-Cardwell et al., 2006). Asian citrus psyllid is of

particular importance because they spread citrus greening disease or Huanglongbing

(Pelz-Stelinski et al., 2010). Huanglongbing (HLB) is one of the most serious problems

in Florida’s citrus due to rapid transmission of the disease within groves and

corresponding rapid decline in productivity of infected trees (Mishra et al., 2012; Quarles

2013). Asian citrus psyllids are of particular importance because represents the vector

for spreading citrus greening disease or citrus Huanglongbing (HLB) (Pelz-Stelinski et

al., 2010). The most common symptoms for HLB disease is the small and misshapen

fruit with bitter taste (Hodge and Spreen, 2012). Further symptoms which shown in the

infected tree is the yellow shoots on the tree (Gomez, 2009).

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Figure 1-1. Citrus greening (HLB) disease symptoms: A represents the yellow shoots on the affected tree; B represents the misshapen fruit on the affected tree. [Adapted from: Gomez, 2009].

Presence of new flush and temperature are the two main factors that affect

psyllid reproduction (Rogers and Stansly, 2006). In addition, 20-30°C is the ideal

temperature conditions for the psyllids, while temperature above 32.22 °C cause

populations to declined (Rogers and Stansly, 2006). Majumdar and Fadamiro (2009)

highlighted the importance of early detection of ACP due to their ability to reproduction

under certain conditions. High populations of ACP can cause direct plant damage

(Halbert and Manjunath, 2004). In order to control the pests, an efficient scouting

method that tracks the pest population over time is required (Stansly et al., 2010). Adult

Asian Citrus Psyllids monitored by using one of the following methods: Tap sampling,

sticky traps, and sweep nets, while the nymphs and eggs of ACP can be detected by

direct observation (Stansly et al., 2010). Stem-tap sampling can be used to monitor the

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adults of Asian citrus psyllids (Hall et al., 2007, Hall and Hentz 2010, and Stansly et al.,

2010). Tap sampling method represented by placing a surface below the branch of the

tree such as a clipboard and hit the branch with a PVC pipe three times and the fallen

adults of Psyllids will be counted and recorded (Stansly et al., 2010).

Problem Statement

One of the most common pests for citrus in Florida are Asian citrus psyllid.

Insects cause two kinds of damage to growing crops: (1) direct physical damage by

eating leaves, fruit and/or roots and (2) indirect biological damage by transmission plant

pathogens (Halbert and Manjunath, 2004). Asian citrus psyllid is of particular

importance because they spread citrus greening disease or Huanglongbing (Pelz-

Stelinski et al., 2010). HLB is one of the most serious problems in Florida’s citrus

industry and worldwide due to its rapid transmission from infected to healthy trees within

and between groves and as a consequences rapid decline in the productivity of the

grove (Mishra et al., 2012; Quarles 2013). In order to control the pests, an efficient

scouting method that tracks the pest population over time is required (Stansly et al.,

2010). Currently the exist methods of insect pest monitoring is time consuming and

labor intensive and costly.

Specific Objectives

The long-term objective of this study is to develop a system for quantifying and

mapping ACP in the citrus groves. The specific objectives of this study are to:

1. To develop a machine for quick and cost effective collection and monitoring of Asian citrus psyllid in citrus groves.

2. To conduct field trials to determine the optimal parameters of the shaking machine.

3. To generate geo-referenced Asian citrus psyllid prediction map.

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CHAPTER 2 LITRATURE REVEIW

Identification of Asian Citrus Psyllid (ACP)

Asian citrus psyllid Diaphorina Citri Kuwayama started was first observed in

Florida and Texas in 1998 and 2001 respectively (Grafton-Cardwell et al., 2006). Asian

citrus psyllids are of particular importance because represents one of the vectors for

spreading citrus greening disease or citrus Huanglongbing (HLB) (Pelz-Stelinski et al.,

2010). The Asian citrus psyllid (ACP) is a tiny mottled brown insect, 3-4 mm in length

with three development stages, from egg through five nymph instars to the adult stage

(Mead and Fasulo, 1998). The psyllid female can lay eggs up to 857 on grapefruit plant

and 572 eggs on rough lemon plant, and it need 16-17 days to reach the adult stage

under 25˚ C (Grafton-Cardwell et al., 2006). The nymphs ranged from 1/100-1/14 inch in

length, yellowish – orange in color (Grafton-Cardwell and Daugherty, 2013). Figure (2-1)

shows the Asian citrus psyllid and their three development stages.

Figure 2-1. Asian Citrus Psyllid and its three different stages: A represents the eggs

stage; B represents the nymphal stage, and C represents the adult stage: [Adapted from: Mead and Fasulo, 1998].

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Ecology of Asian Citrus Psyllid

There are three factors affects the development of psyllids population including:

temperature, relative humidity and rainfall (Teck et al., 2011). Rogers and Stansly

(2006) found that the new flush and the temperature effects on the psyllids population.

They found that the adults of psyllids found on the new flush and the female psyllid lay

the eggs on the young leaves. Furthermore, they found that in the absence of the new

flush, the psyllids, the psyllids are shown on the underside of the leaves. Also, they

found that the ideal temperature for female psyllids is 20-30 ˚C.

Martini et al. (2016) studied the factors affecting the abundance of Asian Citrus

psyllid during winter season in Florida citrus. They surveyed the population at three

heights of the canopy and at four cardinal directions. Their results showed the number

of psyllid was more with canopies facing south than the canopies facing north. Also they

found there are two other factors correlated positively with the psyllid population

including the relative humidity and the emergence of new leaves.

Gutierrez and Ponti (2013) found that the psyllid reproduction occurs in spring

time when the leaf flushes and also they found that the flushing occur after the

harvesting and fruit maturation. However, they found that psyllid reproduction is less

with foliage mature.

Tsai et al. (2002) studied the seasonal abundance of Asian citrus psyllid in

Southern Florida and they found that the highest population was in May, August and

from October through December. In the spring and with very humid conditions, the

nymphs’ development stage is high (Aubert, 1990). Liu and Tsai (2000) found a

significant effects of the temperature ranged from 25-28˚C on the development rate and

the reproduction of Asian citrus psyllid.

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Teck et al. (2011) studied the seasonal population dynamics of the Asian citrus

psyllid. They found that the population peaks for the adult and eggs are in August-

September, February-March and June-July. Also, they found that their population are

correlated with the new flushes and negatively correlated with the temperature and

relative humidity.

Rakhshani and Saeedufar (2011) found that Asian citrus psyllid population

increase with the moderate climate and with new flushes.

Setamou and Bartels (2015) reported edges effects of the field on the Asian

Citrus Psyllid distribution. They found that the edges have a strong effect on the psyllid

distribution. Their results showed the number of psyllids are more on the perimeter

trees. Also, they found that a higher number for psyllids on the trees located on the east

and south sides than those on other sides of the groves. Their study showed also psyllid

density declined significantly with increasing the distance from the edge to the center of

the grove. Furthermore, they found that the field edge with non-surrounding area hold

more psyllids than edges surrounded by other groves.

Monitoring Methods for Asian Citrus Psyllid

The main objective of sampling the insect pest is to detect their population and

their distribution (Zehnder, 2014). Field conditions such as field size, crop and the field

layout size are parameters determine the way of sampling (Zehnder, 2014). When the

field is a squared-shape, ʻʻU’’ sampling pattern is used, while ʻʻW’’ sampling pattern is

used for a long and narrow field (Zehnder, 2014). Majumdar and Fadamiro (2009)

indicated to the importance of early detection of Asian citrus psyllid due to their ability to

reproduction under certain conditions. High populations of Asian citrus psyllids can

cause direct plant damage (Halbert and manjunath, 2004). In order to control the pests,

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an efficient scouting method that track the pest population over the time is required

(Stansly et al., 2010).

Visual Sampling

Visual survey method is used for the new flush to detect the psyllid and the

monitoring procedure should conduct on all the development stages of insect (Crafton-

Cardwell, 2006). Visual survey is used for detect psyllid nymphs and eggs (Stansly et al.,

2010). The method procedure consists of sampling of 10 trees on each of the north, east, south,

west, and the center of the orchard and select a young leaf and examine the flush of that tree

for exiting all the psyllid stages (Grafton-Cardwell, 2016).

Figure 2-2. Visual inspecting for the new flush [Adapted from: Grafton-Cardwell et al.,2006]

Tap Sampling

Tap sampling is a sampling method for psyllid adult and nymphs (Quarles, 2013).

Stem-tap sampling is one of the effective sampling methods for detect Asian citrus

psyllid which started on 2006 (Stansly et al., 2010). In this method two materials should

be used to complete the procedure of this method in four steps, and this material are

white clipboard, and PVC pipe for beating the branches (Stansly et al., 2010). The

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procedure for this method is to place the clipboard beneath the branch and strike the

branch with the pipe three times and then the number of the falling adults on the

clipboard will be counted and recorded (Stansly et al., 2010). The pros of this method is

the low cost and this method is an effective method with high psyllid population, while

the cons of this method is cannot be used for the small trees (USDA /APHIS, 2010).

Figure 2-3. Use of clipboard (left) and number of psyllids on white clipboard (right). [Adapted from: Stansly et al.,2010]

Hall and Hentz (2010) used two different sampling methods: stem-tap and yellow

sticky traps in order to estimate number of adult Diaphorina citri Kuwayama (Hemiptera:

Psyllide). They found that both sampling methods were effective for detecting adults’

psyllids in trees. However, they found that sticky traps sampling is better when adults’

densities are low. Besides, they found that sticky traps can be used for trees with height

less than a meter. Whereas, tap sampling is not accurate method when the number of

adults is large.

Sticky Traps

A yellow or yellow-green sticky cards method is one of the traditional and

effective method for flying adult of Asian citrus psyllid detection (Stansly et al., 2010).

The drawback of this method is the labor cost since the cost for each trap is around $1

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per trap and the labor cost for hung and collect and read for each single trap (Stansly et

al., 2010). The pros of this method are the time required for monitoring the groves using

the sticky traps is less and the method is efficient for early detection, while the cons of

this method is the cost for each trap is approximately $1 and required a technical people

that can read the sticky traps and identify the psyllids (USDA /APHIS, 2010).

Figure 2-4. Use the sticky traps (left) and number of Asian citrus psyllids on the traps (right).). [Adapted from: Stansly et al., 2010]

Atakan and Canhilal (2004) found the number of insects are negatively affected

by the traps height, they reported that the number of whiteflies were captured at 60 cm

were more than 80,100, and 120cm. Gencsoylu (2007) conducted a study on using

yellow sticky cards to monitor some cotton pests at different heights, positions,

dimensions and directions during 2004 and 2005 .He found that the largest population

was captured at 25 cm for Franklineiella spp while for Bemisia and Empoasca spp , the

largest was at height 30 cm above the ground level. For sticky cards position, he found

that vertical position has more effective to capture Empoasca and Frankliniella spp.

whereas for B. tabaci there was no significant differences between the two positions.

Hall et al. (2010) compared six types of sticky cards traps with different colors to

evaluate them for monitoring Asian citrus psyllid in citrus in Florida and Texas. They

26

found a positive correlation between the number of adults captured on traps and the

percent reflectance in the yellow region, whereas, the reflectance in the blue region

there was a negative correlation with number of adults captured on traps. Yen et al.,

(2013) evaluated five sampling techniques for detecting of the tomato potato psyllid,

bactericera cockerelli (sulc) (Hemiptera: psylloidea: Triozidae) in potato crop on the

north island of New Zealand. The five sampling methods was sticky traps, water traps,

sweep netting, vacuum sampling and direct searching). Their results showed that sticky

traps and water traps outperform than other methods for detecting psyllids. However,

there was a minor difference between sticky traps and water traps due to the sampling

variability. Their results showed that the mean parameter estimates for sticky traps and

water traps were positive, whereas was negative for other methods. Premalatha and

Rajangam (2011) investigated the efficacy of yellow sticky traps and yellow charts

coated with castor oil against greenhouse whitefly, Trialeurodes vaporarioum in

gerbera. On the third, fifth, seventh and fifteenth days after initial installation of traps,

they observed the whitefly number on sticky cards and yellow charts. They found that

the yellow charts coated with castor oil coat attracted 220 whitefly adults, whereas 19

adults for yellow sticky cards on the third day in the same variety (Cassiana). Mensah

(1996) conducted three experiments from 1992 to 1994 for monitoring population of

Austroasca Viridigrisea (Paoli) using colored sticky traps. Traps were placed at a height

of either 25, 50, 75, 100, 125 or 150 cm from the ground. The preference test showed

that yellow traps has a strong caught of A. Viridigrisea adults which is 8-12 per trap per

day. Whereas, the lowest number of adults captured were by green, red, deep blue,

black, magenta, and true blue respectively.

27

Sweep Nets

Sampling with this method is based on using a sweep net for capturing and

recording the adult of psyllid. Sweep net is a 15-inches with 6-10 inches tall. The sweep

net procedure is based on swing the sweep net in an 180˚ arc, from right to left, and one

step walk, then sweep from left to right with same arc. The drawback of this method is

the possibility to spread the disease in the grove in case of the disease presence

(Stansly et al., 2010). The pros of this method is the possibility for detection all the life

stages including: eggs, nymphs and adults, while the cons of this method is the time

required for detection and requiring training people (USDA /APHIS, 2010).

Figure 2-5. Using the sweep net for psyllid detection. [Adapted from: Stansly et al., 2010]

Monoz et al. (2015) evaluated and compared five different sampling methods for

detection and monitoring of the Asian Citrus Psyllid including: sticky traps, suction

sampling, sweep net sampling, and visual sampling in citrus groves under different ACP

insecticide management programs. For visual sampling method, it took 2.2 times than a

stem-tap sample. At 0.1 adults per tap density, stem-tap sampling is not the most

reliable method to reach 0.25 “standard error to mean”. They recommended the random

steps than random selection for trees to detect ACP density and other pest population

for cost-effective way. The most sensitive method was the suction sampling while

28

sweep net has similar sensitivity to stem-tap sampling. However, it was time consuming.

ACP detection was more with visual sampling than stem-tap sampling at densities

below 0.013 adult per tap. With moderate to high ACP densities, stem-taps were the

more precise method. Their conclusion is that other sampling methods were more

efficient at low ACP densities than stem – tap samples to achieve 25% precision.

However, stem-taps method can be used when the threshold of 0.1% tap or above.

D Vacuum

This method depends on using a backpack vacuum to suck the insect pest from the

foliage (USDA /APHIS, 2010). The pros of this method is working better with young and

small trees and cons of this method is not efficient with large area and trees, requiring

two people for working and time consuming and labor costly in order to separate the

debris from target insect (USDA /APHIS, 2010).

Suction Traps

This method is based on using vacuum attached to two different heights tubes and the

tube attached to the collection jar (USDA /APHIS, 2010). The pros of this method is can

be used for monitoring psyllids over time and space, while the cons of this method is the

high initial cost to build the traps and the method is not efficient for monitoring the psyllid

with low population (USDA /APHIS, 2010).

Models Proposed to Predict Insect Variation over an Area

Leaf Washing Method

Martini et al. (2012) evaluated sampling method called leaf washing method to

collect and count nymphs of potato psyllids. The main parts of this system are the

vacuum pump, Buchner, carboy, water reservoir, spigot, five polypropylene Buchner

funnels, and pipes. The leaf washing process is started with immersing all samples of

29

infested leaves in cold water to remove dust and sand and then immersing them in hot

water (>85 °C) for five seconds. Later, extracting nymphs from hot water using vacuum

pump to force water goes through fine mesh organza fabric. Then, when removing

organza fabric, they counted psyllids nymphs under a stereoscope. Their results show a

negative correlation between extraction time and water temperature. From their results,

they recommended to use the leaves from the mid portion of the canopy.

Figure 2-6. Leaf washing method filtering device. [Adapted from: Martini et al., 2012].

Shake - and – Washing Technique

Zacharda et al. (1988) developed a shake-and-wash technique for monitoring

mites in apple orchards. This technique depends on removing mites from the plant

materials by adding 300-500 mL of 80-90% ethanol in the jar which containing about 10-

15 leaves, spurs, or shoots with undeveloped leaves and then shake them for 5-10 s.

The shaken is repeated for one more time after a rest of 1 min later, they removed plant

materials using forceps. After mites being settled, the recovered mites counted. Their

results showed that this technique is more efficient by 10-20% than direct count method.

30

Two Simple Insect Sampling Devices

Nishida and Takara (1979) used two different devices for insect sampling. The

first device is the dry shaking device which separated insects from plant materials

through shaken the entire device 10-15 times up and down in vertical direction. Then

the insects will be dropped into the vial at the bottom. The wet shaking device utilized a

washing agent to separate insects from plant materials. The plant sample is placed in

the top of the container and 30% of ethyl alcohol is poured into the container and let the

device shaken up and down in the vertical direction 10-15 times. Later the liquid flow

through the nylon organdy sieve to catch the insects while the washing agent drains to

the container.

Washing Machine for Arthropods Recovery

Leigh et al. (1984) developed a washing machine for Arthropods recovery from

plant leaves. The machines components are: washing tank, solution inflow jets, flushing

jet flow, rate control valves, outflow pipe, sieves, pump. They collected 25 cotton leaves

randomly and put them in that tank for 10 minutes then after that, the leaves were

removed by forceps. The floating spider mite recovered using the screens. The

recovered spider mites in different stages were counted. Besides, they flushed the

washing solution remaining from the washing tank through a sieves for spider recovery.

In order to save the counting time especially when large number of spider mite are

collected. They developed a subsample system which consists of a centimeter grid. The

benefits of this system is that the pump ca be used to supply two or three washing

machines at the same time. Their results showed that with operating two washing

machines at the same time, they can complete five to six samples per hour. Their

31

results showed a high correlation for the spider mites numbers (r=0.92) calculated from

subsamples compared to the total sample counts.

Figure 2-7. Leaf washing machine for Arthropods recovery from plant leaves. [Adapted: from Leigh et al.,1984.]

Three Different Vacuum Devices for ACP Detection

Thomas D.B. (2012) compared three different vacuum devices for Asian citrus

psyllids detection. The first device is a vacuum with AC rechargeable handled while the

second vacuum was a DC model power handled. Two devices were in the same size

and weight and nozzle diameter (32mm). The third device was a leaf blower with

modification for the blower tube to the intake port. They conducted their test in the

Valencia orange groves for Psyllids detection. The basic method used is that the leaves

and the shoots was brushed using the intake nozzles of the vacuum for 5 minutes. They

sampled different area each week, and they finish the sampling within 6 months. Their

results showed that the leaf blower cached more psyllids other devices. However, the

mean differences for the all three devices (AC, DC, and leaf blower devices) were

statistically significant at: 17.4,33, and 96.8 respectively.

32

Figure 2-8. Vacuum devices used for detect Asian citrus psyllid. [Adapted from: Thomas D.B. 2012].

An Automated System for Detection and Extraction Pest in Paddy Field

Miranda et al. (2014) established an automated system for detection and

extraction pest in paddy field. They used a wireless camera with a sticky trap for insect

pests’ detection. Simple and efficient image processing mechanism was used for insect

pests’ detection which based on five different steps including: image acquisition, image

pre-processing, detection of pests in the image, filtering of the image, and extraction of

the detected pests. Their results showed that the system was simple and efficient to

detect the insects in the captured image.

Figure 2-9. Global architectural design. [Adapted from: Miranda et al., 2014].

33

An Autonomous System for Insect Pest Detection

Thangalak and Ramanujan (2015) designed an autonomous system for insect

pests’ detection in three different fields. The system consists of electronic trap with three

different layers and thickness to catch different insects, and the trap covered by the bait

to attract the insects. Also, the system consists of a large plastic bucket with lid, three

different types of mesh, vans, plastic funnel, U-V light to attract the insects, and IR

cameras to capture the image of the trapped insects. The captured image sent remotely

for insect density estimation. Therefore, this information can help the farmer when and

where to use the pesticides.

Figure 2-10. Trap design. [Adapted from: Thangalak and Ramanujan, 2015].

Shaker Machine for Different Applications

Mobile Limb Shaker

Kemp and Melanson (1997) designed a mobile limb shaker for apple harvesting.

The machine consisted of a shaker boom and a hydraulic cylinder to allow the shaker

boom to swing in a horizontal plane. Also it consists of a boom holder, a roller chain,

and hydraulic motor with sprockets. Three different shaking mechanisms were designed

and evaluated including: a double-crank unit (straight line), a single-crank unit built into

the clamp housing (single crank-pivot) and a single –crank unit mounted on the opposite

34

end of the boom (single crank-straight line). Their results showed that the single- crank

unit was the best when considering the effectiveness of fruit removal and mechanical

design. Also, the shaker rate was 20-53 trees/h with more than 90% of fruits removal.

Figure 2-11. Mobile limb shaker for apple harvester. [Adapted from: Kemp and

Melanson ,1997].

Trunk Shaker

Leone et al. (2015) used a trunk shaker to determine the frequency, acceleration

and shaking time on the removal percentage of olives. Their results showed that fruit

removal percentage is positively correlated with frequency and acceleration for all olive

cultivars. The optimal frequencies that maximizes the fruit removal percentage were 25

Hz, 23 Hz and 27 Hz for Frantoio and Picholine, for Leccino and for Cima di cultivars

respectively. The acceleration measured on the trunk was (70.41-99.25 m 𝑆−2) in

range. The optimal shaking time ranged from 6-8 s. They found that increasing the

shaking time beyond the optimal value will not result in increasing the fruit removal

percentage and will effects on the equipment life.

35

Figure 2-12. Olive trunk shaker [Adapted from: Leone et al.,2015].

Vertical Canopy Shaker

Sumner (1973) investigated the effects of the frequency and stroke of the vertical

canopy shaker on the removal of Valencia oranges using two shaker drive systems. The

first drive system was a 112⁄ in –dia recycling hydraulic cylinder and the second drive

system was a hydraulic-driven crank. The frequency used in the first drive system is up

to (225 cpm) while the shaking frequency in the second drive system was up to (350

cpm) with stroke adjustments from 4-12 in. The first test was conducted in 1970 using

the two drive systems (without flywheel). Selected limbs were shaken for 18 sec with

range of (6,9 and 12 in) of stroke displacement. The results of the first test showed that

percentage of the fruit removal was higher with using the crank-driver- shaker than the

cylinder-drive-shaker with low- frequency range. The second test was in 1971 using the

crank- drive – shaker with adding flywheel. The shaker strokes were (6,9 and 12 in) and

the selected limbs was shaken for 10 sec. The results of the second test showed that

the percentage of the fruit removal was reduced. They concluded from their study that

the fruit removal was proportional to the shaking frequency and stroke in 1970 test.

Their conclusion from the test in 1971 (adding the flywheel to the shaker) was that the

influence of the stroke and the shaking frequency is less on the selectivity ratio.

36

Figure 2-13. Shaker unit with crank drive system before adding flywheel. [Adapted from:

Sumner, H.R.1973].

37

CHAPTER 3 EVALUATION OF SAMPLING PATTERNS METHODS TO DETECT AND MONITOR

ASIAN CITRUS PSYLLID IN CITRUS GROVES

Understanding where the insect pest population high is very important for

managing their control across the field and the objectives of insect sampling is to detect

their densities and distribution in the field (Zehnder, 2014).

Identify, locate and determine the infestation severity of pest are the primary

goals of pest monitoring (Schnelle and Rebek, 2016). The crop, field size determines

the sampling procedure, for instance when the field is a square shape then the “U”

sampling pattern is more commonly used, while when the field is long and narrow then

the “Zig-Zag” or” Z” sampling pattern is more appropriate (Zehnder,2014). Sampling

techniques used for insect pest vary depends on the type of insect. Visual sampling,

sweep net, sticky traps, and beating sampling are methods used for sampling Asian

Citrus Psyllid.

Adults of Asian citrus psyllid can be monitored using yellow sticky traps (Hall et

al., 2007). Sticky traps used for Asian citrus psyllid detection provide an evidence of

infestation (Majumdar and Fadamiro,2009).

The main objectives of this study are: Developing sampling techniques in terms

of optimal position (Horizontal, and Vertical) for placing colored sticky traps to monitor

Asian citrus psyllid using different pattern sampling (grid and zigzag patterns), and

generating geo-referenced prediction maps under citrus groves in Florida using three

different interpolation method including: Inverse weighting distance, ordinary kriging

method and simple kriging method.

38

Materials and Methods

The Study Area

Experimental test was performed in organic citrus groves located in winter

Garden, Florida at 28 30' 56.49" N 81 40' 10.49" W on April and May of 2015. The area

size used for the field experiment was 1.19 ha (2.94 ac) as shown in Figure 3-1 from the

total area size of 3.84 ha (9.50 ac).

Figure 3-1. Study area.

Insect Sampling Procedures:

In order to evaluate the differences of commonly used methods for sampling

patterns for insect pests (Asian citrus psyllid), different sampling techniques

implemented to reflect the population density. Yellow sticky traps were used to monitor

adult ACP (Stansly et al., 2010). Two different patterns of sticky traps distribution used

to monitor psyllids population. These patterns include: Regular grid pattern and the

39

regular zigzag pattern as shown in the Figure 3-2. Special sticky trapes placed at

specific location in the field to detect their activities. Two trapes placed at each tree with

two different positions (horizontal and vertical) as shown in Figure 3-3 to trap the pests

in citrus orchard to see which placement is more effective to catch insects. Traps set

approximately at the middle height of the tree. The traps were aligned in a south to

north direction. Insect sampling, sample locations georeferenced, then by looking at the

insect count results, it is possible to correlate the insect population with spatial data. By

using Real time kinetic (RTK) GPS system, the location of each Ground Control Points

(GPS) is defined on a topographic map. The number of adult psyllid captured on each

sticky traps are counted manually and then analyzed. The interpolation process was

applied to the datasets using three different interpolation methods including: Inverse

Distance Weighting (IDW), ordinary kriging (OK) and simple kriging (SK) to estimate

unknown ACP population in the neighborhood. Geostatistical analyses were employed

using the geostatistical analyst extension in ArcMap (ArcGIS 10.3.1 © 1999-2015 Esri

Inc.

IDW interpolation estimates the unknown values with a mathematical formula

from the nearby knowing values as shown in equation 3-1and 3-2 (Yasrebi et al., 2009):

Z(x)=∑ 𝑊𝑖 𝑍𝑖

∑ 𝑊𝑖

(3-1)

𝑊𝑖 = 1

𝑑𝑖𝑝 (3-2)

40

Where: Z: is the unknown value, and 𝑊𝑖: is the measured value, and 𝑑 : is the distance

between the sample point and the unknown point.

P: power parameter.

Equation 3-3 (Yasrebi et al., 2009) used to estimate the predicted value using

ordinary kriging method.

Z(x)= ∑ 𝜆𝑖 𝑍𝑖𝑁𝑖=1 (3-3)

Where:

Z(x): The predicted value, and Zi: The measured value at location I, with 𝛌: unknown

weight for the measured value at the i location, and N: The number of measured values.

The measure of the accuracy, called the Root-mean -squared error (RMSE)

(Karydas et al.,2009) is the measurement will be used for testing the prediction map.

Equation 3-4 shows the mathematical formula used for RMSE estimation (Odeh et al.,

1994).

RMSE = [ 1

𝑛 ∑ (|𝑧(𝑥𝑖 ) − ��𝑛

𝑖=1 (𝑥𝑖 )|)2]1

2⁄ (3-4)

Where:

RMSE: Root-mean –Squared-Error.

Z (Xi): the observed value at location I.

��(Xi): the predicted value at location I.

N: the sample size

41

Cross validation can be used to evaluate the results of interpolation techniques

using different sampling methods. Finally, the relative improvement (RI) of the best

method can be calculated using Equation 3-5 (Yasrebi et al., 2009).

RI = 100 |𝑅𝑀𝑆𝐸𝑏𝑒𝑠𝑡−𝑅𝑀𝑆𝐸𝑐𝑢𝑟𝑟𝑒𝑛𝑡|

𝑅𝑀𝑆𝐸𝑏𝑒𝑠𝑡

(3-5)

Where:

RI: Relative improvement

𝑅𝑀𝑆𝐸𝑏𝑒𝑠𝑡 : The minimum value of RMSE

𝑅𝑀𝑆𝐸𝑐𝑢𝑟𝑟𝑒𝑛𝑡: The RMSE of the current model

The state plane projection and North American Datum 1983(NAD 83) specified

the location of the study area in metric units. The results of the interpolation were

represented over the study site map which shown in the next section.

Figure 3-2. Two different patterns of sticky traps distribution used to monitor psyllids population. A) represents grid sampling pattern and B) represents zigzag sampling pattern.

42

Figure 3-3. Two positions for traps' placement.

Figure 3-4. Distribution of sample points representing the location of Asian citrus psyllid monitoring reference points.

In the Figure 3-4 each symbol in the point layer represents a location where the

ACP has been measured. 18 tree locations were randomly selected in the field to take

the measurements and represented as reference points which used for maps accuracy

assessment. The general procedure for collecting the true ground truth for ACP was

placing one trap on each selected tree and leave it for one week. Traps set

approximately at the middle height of the tree. The traps were aligned in a south to

north direction. The number of adult psyllid captured on each sticky traps are counted

manually.

43

Results and Discussion

Statistical Analysis

In terms of data analysis, to analyze the results of the experiment using IBM

SPSS statistics version 23 (IBM Corp. copy right IBM corporation 1898, 2015). A

compare mean / one –way ANOVA was used to determine significant differences at P =

0.05. The results of the analysis for selected sticky traps at different positions

(horizontal and vertical) are shown in Table 3-1.

Table 3-1. Analysis of variance of data on the number of psyllids captured at different trap position (horizontal and vertical).

Source Sum of squares df Mean square F Significance

Sticky trap position 9.481 1 9.481 0.447 0.505 Error 2249.185 106 21.219 Total 2258.667 107

** Significant at P< 0.05

Table 3-1 shows that the effect of two trap positions in capturing ACP was not

statistically significant as determined by one-way ANOVA (F (1,107) = 0.447, P= 0.505.

Figure 3-5 shows mean number of ACP captured using two different traps positions.

The orange boxplot represents horizontal traps position, while the blue boxplot

represents vertical traps position. It can be seen from the mean for both datasets are

approximately around (5). However, there is a little more variation in vertical position

which approximately ranges from o to 16, whereas horizontal position ranges

approximately from 0 to 15.

44

Figure 3-5. The plot of number of psyllids captured by different traps position: traps

positions is represented as: orange box plot, horizontal position; blue box plot, vertical position.

Exploratory Statistics for Asian Citrus Psyllid under Different Sampling Patterns with Different Traps Position

The summary statistics of ACP distribution using different sampling methods with

different traps position are shown in Table 3-2. The positive value of skewness and

kurtosis values for the grid sampling method are (0.802-1.001) and (2.814-4.398)

respectively. While under zigzag pattern sampling, the skewness values and kurtosis

values are (1.276-2.33) and (4.212-9.807) respectively.

Table 3-2. Descriptive statistics for ACP distribution using different sampling methods Sampling method

Traps position

Min

Max

Mean

Std.dev Skewness

Kurtosis

Median

grid pattern horizontal 0 18 6.087

4.055 1.001 4.398 5

grid pattern vertical 2 17 7 4.338 0.802 2.814 7 zigzag pattern Horizontal 2 17 6.19

3 3.936 1.276 4.212 5

zigzag pattern vertical 0 30 6.548

5.789 2.33 9.807 6

The prediction error mean and the root- mean- squared prediction error can be

seen in the Figure 3-6 and Figure 3-7 for all sampling patterns with different traps

positions using three different interpolation methods .Figure 3-6 shows that the

45

prediction error using IDW, Ordinary kriging and simple kriging is -0.135,-0.098 and -

0.025 respectively whearas for RMSR is 4.80,4.92 and 4.656 respectively for grid

pattern . While for the zigzag pattern, the prediction error using IDW, Ordinary kriging

and simple kriging is -0.416,-0.457 and -0.377 respectively and for RMSR is

4.029,3.968 and 3.870 respectively and as shown in the Table 3-3. The reason behind

why we get different results with different sampling patterns which resulted in different

prediction maps is the interpolation techniques used are different methods .

Furthermore, the denisty and the spacing of the sample points will effects on the results

and then on the accuracy of the prediction map. It can be seen that the density and the

spacing of the samples using grid sampling is totally different from using the zigzag

pattern, in turn this will effects on the prediction maps outputs .

Table 3-3. The means of mean (M) and root mean square error (RMSE) for different traps placement under two different sampling patterns.

Sampling pattern

Interpolation method Prediction error

M RMSE

grid pattern Inverse weighting distance

-0.135 4.80

Ordinary kriging -0.098 4.90

Simple kriging -0.025 4.656

zigzag pattern Inverse weighting distance

-0.416 4.029

Ordinary kriging -0.457 3.968

Simple kriging -0.377 3.870

46

Figure 3-6. The cross validation comparison of the ACP distribution map by different interpolation methods using grid pattern.

Figure 3-7. The cross validation comparison of the ACP distribution map by different

interpolation methods using zigzag pattern.

Comparison of the Interpolated Maps by the Two Sampling Patterns and Two Different Traps Position

The spatial distribution of Asian Citrus Psyllid is shown in the Figures (3-8,3-9,3-

10 ,3-11,3-12 and 3-13) using grid sampling pattern with two different traps position.

While Figures (3-14,3-15,3-16 ,3-17,3-18 and 3-19) using zigzag sampling pattern with

two different traps position which is due to many factors because the insect distribution

affected by the insects’ development stage, the season, the weather conditions. Each

prediction map provide insect population distribution represented by a specific color on

47

the map which is shown in the accompanying key. Figures 3-8,3-9, and 3-10 show the

distribution map for Asian Citrus Psyllid using sticky traps distributed in grid pattern

under horizontal position. The image shows that the large number of psyllid caught by

horizontal position of traps in the west and north side of the field which confirm the edge

effects fact. While the Figures 3-11,3-12, and 3-13 show the distribution map for Asian

Citrus Psyllid using sticky traps distributed in grid pattern under vertical position. The

image shows the large number of psyllids caught by the vertical position of the traps are

shown in the middle of the field. Figures 3-14, 3-15, and 3-16 shows the distribution

map for ACP using sticky traps distributed in zigzag pattern with the horizontal traps

position. The image shows that the high density of psyllid is more distributed in the

western north edge of the field and also in the center. In contrast, it can be seen that

there is different trend of psyllids distribution in the field using zigzag pattern using

sticky traps in vertical position as shown in Figures.3-17,3-18, and 3-19. The images

show that the ACP is more distributed in the north of the field. The possible

explanations for the high infestation with adult psyllids in certain areas of the field is the

new flush and the temperature which effects on the psyllids population (Rogers and

Stansly ,2006) and psyllid population correlated positively with the relative humidity and

the emergence of new leaves (Martini et al.,2016). Furthermore, the edges of the field

have a strong effect on the psyllid distribution (Setamou and Bartels, 2015)

48

Figure 3-8. Distribution of Asian citrus psyllid under grid pattern with horizontal traps position using Inverse distance weighting method.

Figure 3-9. Distribution of Asian citrus psyllid under grid pattern with horizontal traps position using ordinary kriging method.

49

Figure 3-10. Distribution of Asian citrus psyllid under grid pattern with horizontal traps

position using simple kriging method.

Figure 3-11. Distribution of Asian citrus psyllid under grid pattern with vertical traps

position using Inverse distance weighting method.

50


position using ordinary kriging method.



51

Figure 3-14. Distribution of Asian citrus psyllid under zigzag pattern with horizontal traps

position using inverse distance weighting method.



52



Figure 3-17. Distribution of Asian citrus psyllid under zigzag pattern with vertical traps

position using inverse distance weighting method.

53




position simple kriging method.

54

Measures of Accuracy and Effectiveness of Prediction Maps

Two indices were calculated from the measured and interpolated values at each

selected location for each test data set. Root mean squared error (RMSE) and relative

improvement (RI) considered for measuring the accuracy and the effectiveness of

prediction map. The results from table 3-4 showed that simple kriging interpolation

method is the best predictor for all different sampling methods and different traps

position because it is provided the least value of RMSE compared to other interpolation

methods. Furthermore, from the Table 3-4, it can be seen that using simple kriging

interpolation method with zigzag sampling pattern under horizontal traps position gave

us more reliable prediction since the root mean squared error is the least value (RMSE

= 5.73) compared to other interpolation methods and with other sampling pattern.

Furthermore, the best value of relative improvement was when using zigzag pattern with

horizontal traps position (RI%=0.0%). The results show that the best value of the two

indices was when using a zigzag pattern sampling with horizontal traps position.

Table 3-4. Accuracy and effectiveness measurements for the sampling patterns methods by using different interpolation methods.

Sampling patterns Traps position Interpolation method RMSE RI (%)

Grid pattern Horizontal Inverse Distance weighting 9.38 62.85

Ordinary kriging 9.47 65.37

Simple Kriging 9.33 63.85

Vertical Inverse Distance weighting 6.95 17.44



Zigzag pattern Horizontal Inverse Distance weighting 6.33 10.50



Vertical Inverse Distance weighting 8.57 50.0



55

The conclusions of this study can be summarized as follow: monitoring with

sticky traps and the use of spatial maps of ACP population distribution are two important

tools to detect Asian Citrus Psyllid and predict their infestation. Sticky traps method and

GIS technology were used to estimate Asian Citrus Psyllid population in non-organic

citrus groves in Florida in summer 2015. The maps developed in this study can serve as

a database to find out the better insect pest sampling pattern which provide the less

prediction error and better validation. In addition, these maps can assist the citrus

producers in implementing pest management strategies because knowing the exact

locations of high infestation by ACP is important to restrict insecticide applications only

to places where they are necessary. The maps obtained from inverse distance

weighting using different sampling patterns with different traps positions show clearly

the map from zigzag sampling pattern with vertical traps position is better than the other

sampling pattern since its high accuracy of prediction map. Therefore, the zigzag

sampling pattern with horizontal traps position along with using simple kriging method

for interpolating Asian citrus psyllid population distribution is the most accurate one.

It can conclude from the output of the field experiment that the zigzag pattern

sampling technique along with horizontal traps position provides a best estimation for

Asian Citrus Psyllid infestation and their distribution over study area.

56

CHAPTER 4 EVALUATION OF MEASURED ACCELERATION PRODUCED BY THE

CONVENTIONALTAP METHOD AND A DEVELOPED SHAKING MACHINE FOR ASIAN CITRUS PSYLLID SAMPLING

Tap sampling method is one of the manual techniques used for monitoring Asian

Citrus Psyllid. Tapping is commonly recommended for monitoring adults of ACP. Tap

sampling is one of the effective sampling methods for detect Asian citrus psyllid (Stansly

et al., 2010). The procedure for this method is to place the clipboard beneath the branch

and strike the branch with the pipe three times and then the number of the falling adults

on the clipboard will be counted and recorded (Stansly et al., 2010)

The ultimate goal of this study is to develop a machine for quick and cost

effective mapping and monitoring psyllid population density and distribution in citrus

orchard and specific goal was to identify the vibration characteristics of tap method and

develop a machine that can simulate the same vibration characteristics. This study

determined the relationship between striking the branches and acceleration and if a

relationship exists between the length of time the branches are struck and acceleration.

Also, the optimal shaking frequency was determined by using the developed shaking

machine.

Materials and Methods

In this study two experiments were performed in the same study area. Both experiments

will be discussed in more details in this chapter.

Tap Method Experiment

Study Site. The experiments were conducted in citrus grove at the Citrus

Research and Education Center in Lake Alfred, University of Florida located at 28° 105'

84'' N 81 71' 56'' W on December 2015 - February 2016. The trees are Valencia and

57

was planted in 2012. The total area of the research plot used in this study was 0.655 ha

(1.62 ac).


Tap Method Experiment

A modified version of the tap sampling method of Stansly et al., 2010 was

performed, with the exception that no insects were collected. Accelerometer sensors

were placed on the limb in order to sense and report the acceleration. A rod (Figure 4-2)

was used to strike the branch for fixed length of time (5, 10, and 15 s). The diameter

(𝑑𝑒) and length (𝑙𝑏) of each limb as well as the canopy diameter (𝐷𝑐) were measured

(Figure 4-3). The three trials were performed at different shaking duration for three limb

s lengths. The hitting frequency and acceleration were collected for each limb. The

procedure for the measurements was repeated three times to achieve precise results

which resulted in a 27 samples in total. As shown in Figure 4-4, the accelerometer

sensors were attached to a programmed Arduino UNO R3 board as data logger which

is attached via a USB port on a laptop to record the data using open source Cool Term

software (Version 1.4.6). The data collected using the data logger then were corrected

by removing the unwanted buffered data from beginning and the end part of the data

58

package. Then, developed code on MATLAB 2016a (R 2016a, 9.0.0.341360, 1984-

2016) software was used to produce the resultant from raw data, visualize and analyze

the processed data. The setup of the tap method experiment showed in the Table 4-1.

Figure 4-2. Rod used for shaking the limbs.

Figure 4-3. Experimental setup.

59

Figure 4-4. Circuit diagram for connecting accelerometer sensors to Arduino UNO R3

and connecting Arduino UNO R3 to a laptop via a USB cable.

The duration of the shaking and the limb length were selected as two important

parameters to define the acceleration exerted on the limb using the tap method as

shown in the table 4-1.

Table 4-1. Setup of the tap method experiment

Shaking duration (s) Limb length (m) No. of samples

5 0.889 9

10 0.990 9

15 1.143 9

The resultant acceleration (𝑚 ∙ 𝑠−2) was calculated using Equation (4-1) (Snieder,

2004).

𝑎𝑟 = √𝑎𝑥 2 + 𝑎𝑦

2 + 𝑎𝑧2 (4-1)

Where:

𝑎𝑟 is resultant acceleration (𝑚 ∙ 𝑠−2),

𝑎𝑥is acceleration along the x-axis (𝑚 ∙ 𝑠−2),

𝑎𝑦 is acceleration along the y-axis (𝑚 ∙ 𝑠−2),

60

𝑎𝑧 is acceleration along the z-axis (𝑚 ∙ 𝑠−2).

Accelerometer

A microelectromechanical accelerometer (Freescale Semiconductor, 2008) with

low voltage applied to it was used to sense and record the acceleration in three

perpendicular directions for each limb (Table 4-2). The features of the accelerometer

are: low voltage operation of 2.2-3.6 V, maximum acceleration for all axes of ± 2000 g,

supply voltage range of -0.3 to +3.6 V, and the low cost. The accelerometer

measurements can be acquired in units of m/s^2 or in g-forces (g).

Table 4-2. Accelerometer parameters

Parameters Target value

Number of axes 3 Maximum acceleration amplitude ± 6 g Acceleration resolution 0.01 g

Determination of Tree Limb Parameters

The parameters of the selected tree limbs were measured and described in the

Table 4-3.

Table 4-3. Tree limb parameters

Limb length, 𝐿𝑏 (m)

1st (0.889) 2nd (0.990) 3rd (1.143)

Start point diameter (𝑑𝑐) 0.020 0.031 0.027

Endpoint diameter (𝑑𝑒 ) 0.008 0.014 0.009

Canopy diameter (𝐷𝑐) 0.317 0.317 0.317

Hitting distance (𝐿𝐻) 0.635 0.660 0.812

Data Extraction

The raw data from the accelerometer sensors were converted to decimal values

using the program code developed on MATLAB 2016a (R 2016a, 9.0.0.341360, 1984-

2016). Then, a code on MATLAB 2016a software was used to produce the acceleration

61

resultant from raw data, visualize and analyze the processed data. The graph in Figure

4-5 shows the three components of the acceleration in Cartesian coordinates. The three

Figures (a, b, c) show the acceleration along the x-axis (g), y-axis (g), and z-axis (g).

While the Figure (d) represent the resultant of acceleration(𝑎𝑟). From the Figure 4-5,

the maximum acceleration along the x-axis, y-axis is (4.8 g) and (5.8 g) for the z-axis.

From the Figure, the maximum resultant acceleration is approximately (8.3 g). Figure 4-

6 shows the statistical analysis peaks of resultant acceleration which including the

histogram, boxplot, and the normal distribution. Figure. 4-6 display the frequency of

resultant of acceleration peaks (𝑎𝑟 ). From the presented boxplot of the acceleration

resultant peaks, we can determine the median and mean of the resultant of acceleration

peaks. Moreover, the frequency of repeating same values of resultant’s acceleration

peaks can be obtained from the histogram. This is found that although the frequency is

higher at peak of accelerations about (1.4g) and (8g), the peak of normal distribution

curve is occurring about (4.5 g) which is summation of majority of peaks are happening

around this domain.

62

Figure 4-5. Acceleration wavelength: (a) 𝑎𝑥 [g]; (b) 𝑎𝑦[g]; (c) 𝑎𝑧[g]; (d) 𝑎𝑟 [g].for the tap

method.

63

Figure 4-6. Statistical analysis peaks for resultant acceleration including: Histogram, Boxplot, and normal distribution.

Shaking Machine Experiment

Mechanizing insect pest scouting is a new step can be taken into considerations.

Thus, a shaking machine was built to achieve the goal. The details of the machine will

be explained below.

Development of Limb Shaker

Design and Construction

The aim of this research was to develop a shaking machine for pest sampling

and monitoring. Work involved building a prototype of a mechanical limb shaking

machine and then evaluating it in citrus groves. The machine was designed and built in

the workshop of the University of Florida’s Citrus Research and Education Center

(CREC) in Lake Alfred, University of Florida.

Shaking machine design should follow these criteria:

64

1. The shaker will be mounted on the tractor and the shaking height adjusted through the hydraulic system of the tractor by the tractor operator.

2. The operation of the shaking system should be hydraulically operated through the hydraulic system of the tractor.

3. The shaking arm speed should be controlled by the operator manually through the control value of the hydraulic system.

4. The shaking machine will be operated on one tree and it has only one shaking mechanism.

5. The shaking machine will be operated using the hydraulic power of the tractor through hydraulic hoses connections.

6. The shaking machine operate between rows and near the selected tree to collect the data.

7. The new concept for this machine is the shaking action will be applied only on the selected limb.

The shaking machine comprised four main parts: Main frame, a hydraulic

system, power transmission system, and the shaking system. The dimensions of the

shaker frame were 1.19 m width and 0.55 m height. To mount the shaking unite, an

attachment at the front of the tractor was made to hold the shaking machine temporarily.

The main frame was attached to the main frame of the tractor using two latches, one

latch is on the left side of the frame and the other one is on the right side of the frame as

shown in Figure 4-7.

65

Figure 4-7. The front frame of the tractor attached to the main frame of the shaking machine.

The hydraulic system is composed of two main parts: a hydraulic motor and the

flow control valve. The hydraulic motor was powered by the hydraulic system of the

tractor and was mounted beneath the transmission mechanism housing as shown in

Figure 4-8. The output shaft of the hydraulic motor is attached to the driving sprocket of

the transmission system of the shaking machine. A manual hydraulic flow control valve

allowed control of the rotational speed of the driving sprocket. The adjustable flow

control valve was used to adjust the shaking frequency of the shaking arm (0.26 Hz,

2.33Hz and 4.00Hz) as shown in Figure 4-8.

66

Figure 4-8. The components of the hydraulic system of the shaking machine: A) shows the hydraulic motor; B) shows the flow control valve.

The transmission system consists of two sprockets and a single crank unit

enclosed in a housing. To operate the shaking arm, a sprocket and chain mechanism

with a ratio of 1:2 was used between the hydraulic motor and the crank shaft to operate

67

the shaking arm under different frequencies The Driving sprocket was powered by the

hydraulic motor and the rotational motion of the driven sprocket was converted into

linear motion using single -crank unit as shown in Figure .4-9. The driving sprocket with

30 teeth, while the driven sprocket with 15 teeth. The distance between the driving

sprocket and the driven sprocket is (0.20 m) and the length of the crank unit is (0.38m)

as shown in Figure 4-10.

Figure 4-9. Top view of the transmission system of the shaking machine.

68

Figure 4-10. Top view schematic of the transmission system and the shaking system

The shaking system consisted a box of (0.38 m) long, (0.61 m) width and (0.61

m) height. Inside the box, there is embedded part which is attached directly to the crank

shaft. The embedded part is attached to the box through two pivoted points from the top

and bottom of the box. One piece of shaking arm made of plastic with (1.23 m) long as

shown in Figure 4-11. and can be attached directly to the box frame through one-point

hitch linkage as shown in Figure 4-12. The shaking arm is pivoted at one end to allow

the shaking arm moving in semi arc around the pivot point.

Figure 4-11. Rod used for shaking the limbs.

69

Figure 4-12. Components of shaking system.

Since the primary goal of building the shaking machine is to monitor the insect

pest. Then, a clipboard was built to collect the psyllids. The clipboard plate with

dimensions (0.25×0.38 m) made from aluminum attached to the stick with length (0.56

m) as shown in Figure. 4-13. The stick attached to the bottom of the box as shown in

Figure 4-14. The clipboard placed beneath the shaking arm with a distance of (0.33 m)

70

from the shaking arm. A sticky trap will be placed above the clipboard during the field

test as shown in Figure 4-15.

Figure 4-13. Clipboard tool and its dimensions.

71

Figure 4-14. Side view of the shaking machine showing the shaking arm.

Figure 4-15. Components of the shaking machine.

72

Field Experiment

To find the optimal system operation, an experiment was conducted using three

shaking frequency (0.26, 2.33 and 4.00 HZ) and three shaking duration (5, 10, and 15s).

The duration of the shaking and the shaking frequency were selected as two important

parameters to define the acceleration exerted on the limb using the developed shaking

machine. First, the frequency of the shaking arm was selected through the flow control

valve in order to determine the speed of the shaking arm. The speed of the driving

sprocket was measured using Digital Tachometer (Model CDT-2000HD). The error

measurement using digital tachometer ranged from (0.04 – 0.08). The shaker was

operated between the rows outside the trees. Accelerometer sensors were placed on

the limb in order to sense and report the acceleration exerted on the limb during shaking

procedure. A shaking arm was used to strike the branch for fixed length of time (5, 10,

and 15 s) under different shaking frequencies. The procedure for the measurements

was repeated three times to achieve precise results which resulted in a 27 samples in

total. As shown in Figure 4-16, the accelerometer sensors were attached to a

programmed Arduino UNO R3 board as data logger which is attached via a USB port on

a laptop to record the data using open source Cool Term software (Version 1.4.6). The

data collected using the data logger then were corrected by removing the unwanted

buffered data from beginning and the end part of the data package. Then, a code on

MATLAB 2016a software was used to produce the resultant acceleration from raw data.

73

Figure 4-16. Experiment Setup

Table .4-4. Parameters of the limb and sensor location for the shaking machine experiment

Start point diameter (𝑑𝑐) 0.025 m

Endpoint diameter (𝑑𝑒 ) 0.007 m

Canopy diameter (𝐷𝑐) 0.736 m

Limb length, 𝐿𝑏 0.939 m

The distance of the sensor( 𝐿𝑠) 0.431 m

Table 4-5. Setup of the shaking machine method experiment

Shaking duration (s) Shaking Frequency(Hz) No. of samples

5 0.26 9

10 2.33 9

15 4.00 9

Data Extraction

The raw data from the accelerometer sensors were converted to decimal values

using the program code developed. Then, a code on MATLAB 2016a software was

used to produce the resultant acceleration from raw data.

Figure 4-17 shows the statistical analysis peaks which including the histogram,

boxplot, and the normal distribution using the shaking machine. Figure 4-17 display the

74

frequency of resultant acceleration peaks (𝑎𝑟 ). From the presented boxplot of the

resultant acceleration peaks, we can determine the median and mean of the resultant

acceleration peaks. Moreover, the frequency of repeating same values of resultant

acceleration peaks can be obtained from the histogram. This is found that although the

frequency is higher at peak of accelerations about 0.7, 1.3 and 1.7g., the peak of normal

distribution curve is occurring about 1.9 g which is summation of majority of peaks are

happening around this domain. The graph in Figure 4-18 shows the three components

of the acceleration in Cartesian coordinates. The three Figures (a, b, c) show the

acceleration along the x-axis (g), y-axis (g), and z-axis (g). While the Figure (d)

represent the resultant acceleration in r. From the Figure 4-18, the maximum

acceleration along the x-axis, y-axis is (3 g) and (5.1 g) for z-axis. From the Figure, the

maximum acceleration resultant is approximately 5.2 (g).

00

10

15

20

25

1 2 3 4 5 6 7 8 9 10

5

Peaks of ar [g]

Fre

quen

cy

Average

Median

Histogram

Normal Distribution

Figure 4-17. Statistical analysis peaks including: Histogram, Boxplot, and normal distribution.

75

6

8

10

12

4

2

0

Time [s]5 10 150

(d)

ar

[g

]

0

2

4

6

-2

-4

-6

Time [s]5 10 150

(b)

ay

[g

]

0

2

4

6

-2

-4

-6

Time [s]5 10 150

(a)a

x [

g]

0

2

4

6

-2

-4

-6

Time [s]5 10 150

(c)

az

[g

]

Figure 4-18. Acceleration wavelength: (a) 𝑎𝑥 [g]; (b) 𝑎𝑦[g]; (c) 𝑎𝑧[g]; (d) 𝑎𝑟 [g].for the

shaking machine method.

Results and Discussion

Tap Method Experiment Results

In terms of data analysis, to analyze the results of the experiment using IBM

SPSS statistics version 23. A univariate generalized linear model (GLM) was used to

determine significant difference at 0.05 significance level. The results of the analysis of

variance (ANOVA) for the effects of shaking duration and the limb lengths on the

acceleration are shown in Table 4-6.

76

Table 4-6. The output of the ANOVA analysis for shaking time and limb length using the tap method.

Source Sum of Squares

df Mean Square

F Significance

Limb 3.070 2 1.535 1.782 0.197

Time 0.673 2 0.337 0.391 0.682


The products of three limb lengths (0.889, 0.990, 0.143 m) and three shaking

durations (5, 10, 15 s) were analyzed using IBM SPSS statistics version 23 to study the

effect of shaking time and limb length and their interactions as function of the amount of

acceleration under the experimental conditions. As shown in Table 4-6, according to the

ANOVA, there were non-significant main effect of limb length F (2, 18) = 1.782, p = 0.05

on the amount of the acceleration. For the shaking duration, there was no statistically

significant effect on the acceleration, F (2, 18) = 0.391, p = 0.05. Figure 4-19 displays

the effect of limb length and shaking duration on acceleration. The red line represents

the limb with length 1.143 m, green line represents the limb with length 0.990m and the

blue line represents the limb with length 0.889 m under shaking for different durations

(5,10 and 15 sec).

Shaking Machine Experiment Results

A univariate GLM was used to determine if a significant difference exists at 0.05

level of significance between the shaking frequency and the shaking duration and

acceleration. The results from Table 4-7 shows that there are significant differences for

the shaking duration F (2, 18) =6.258, p=0.05, and the shaking frequency has a

significant difference on the acceleration (2, 18) =418.532, p=0.05 since the force

applied on the limb through shaking procedure will increase with increasing the shaking

frequency of the shaking arm (increasing the speed of the shaking arm).

77

Figure 4-19. The plot of acceleration for each combination of groups of shaking time and limb length. Limb length is represented as: red line, 0.889 m; green line, 0.990 m; and blue line, 1.143 m.

Figure 4-20 is a multiline graph which shows the relationship between the

shaking frequency, shaking duration and acceleration. Figure 4-20 shows that shaking

the limb at a frequency of 4.00 Hz results in a higher acceleration than shaking the limb

at 0.26 Hz and 2.33 Hz. Also, there is a significant difference in acceleration while

shaking the limb with different shaking duration. From Figure 4-20 it can be seen that

the first shaking frequency level (0.26 Hz) has very little effect on the acceleration. The

average acceleration exerted on the limb using shaking frequency of (0.26 Hz) with (5

78

sec) duration is approximately (0.066), with shaking for (10 sec) the acceleration

average is (0.071 g) and for (15 sec) shaking the acceleration average is about (0.072

g). While shaking the limb at (2.33 Hz) was the better as they resulted in significantly

higher acceleration than at (0.26 Hz). The average of acceleration exerted on the limb

using (0.26 Hz) with (5 sec) duration is (0.896 Hz), with (10 sec) shaking, the average of

acceleration is (0.759 g) and for shaking (15 sec) the acceleration average is (0.711 g).

The highest level of acceleration resulted from shaking the limb with a frequency of

(4.00 Hz). The acceleration average of shaking for (5 sec) is about (2.523 g), for

shaking the limb (10 sec) the average of acceleration is about (2.014) and shaking the

limb for (15 sec), the acceleration average is (1.98 g).

Figure 4-20. The plot acceleration for each combination of groups of shaking time and

shaking frequency. The blue line represents shaking the limb for 5 s, while the red line represents shaking the limb for 10 s, and the green line represents shaking the limb for 15 s.

79

Table 4-7. The output of the ANOVA analysis for shaking duration and shaking frequency using the shaking machine.

Source Sum of Squares df Mean square F Significance

Time 0.308 2 0.154 6.258 0.009 Frequency 20.614 2 10.307 418.532 0.000


The conclusion of the study using the tap method can be summarized as follow:

Two studies were conducted to find the relationships between shaking duration, the

length of the tree limb, and the shaking frequency on acceleration. A total of 54 samples

were selected randomly in a citrus grove in Lake Alfred to accomplish the objectives of

both studies.

1. With regards to the limb characteristics, it was found that the limb length does not have a significant effect on the acceleration performed on the limb at 0.05 level of significance.

2. Further studies are needed to investigate the effect of other important parameters such as the diameter of the limbs on the required acceleration.

The conclusions of shaking machine study can be summarized as follows:

1. The shaking machine can be lowered and raised by the hydraulic system of the tractor.

2. The speed and the height of the shaking arm can be adjusted by the operator of the tractor.

3. The ease of positioning the shaking arm aided in minimizing the time required to shake the selected trees.

4. The effect of the shaking duration and shaking frequency on the acceleration was significant at 0.05 level of significance.

5. As the shaking frequency increased the acceleration increased; however, the frequency of 0.26 Hz resulted with low acceleration.

6. For next experiment, it is recommended to use frequency of (2.33 Hz) which can give a better acceleration required for falling psyllids onto the clipboard.

80

CHAPTER 5 UTILIZING A DEVELOPED SHAKING MACHINE AND DIFFERENT INTERPOLATION

TECHNIQUES FOR MONITORING AND MAPPING ASIAN CITRUS PSYLLID

The density of pest populations is varied spatially at any time within a field since

the factors affecting such as landscape, soil, crop, and environmental factors are not

constant (Park et al.,2007). Interpolation plays a role in predicting the unknown values

from sample points (ESRI, 2016). Inverse distance weighting (IDW), Kriging (K), Natural

neighbor, Spline, Topo to raster and trend are the common and available interpolation

methods have been used (ESRI, 2016).

Inverse distance weighting is a deterministic, nonlinear interpolation method. This

method based on estimating the unknown value at unsampled locations from the

weighted average of the sample points of neighborhood (Gruver and Dutton, 2014).

There are some factors need to be taken in to our considerations when using inverse

distance weighting technique including: the relationship of the phenomenon and the

distance, and the size of the neighborhood (Gruver and Dutton, 2014). The output of the

prediction method using this technique can be influenced by the spacing and the density

of the sample points (Gruver and Dutton, 2014). IDW interpolation estimates the

unknown values with a mathematical formula from the nearby knowing values as shown

in Equation 5- 1and 5-2 (Yasrebi et al., 2009):

Z(x)=∑ 𝑊𝑖 𝑍𝑖

∑ 𝑊𝑖

(5-1)

𝑊𝑖 = 1

𝑑𝑖𝑝 (5-2)

Where:

81

Z: is the unknown value.

𝑊𝑖: is the measured value.

𝑑 : is the distance between the sample point and the unknown point.

P: power parameter.

Ordinary kriging is one of the kriging interpolation methods. This method uses

smivariogram or covariance to define the autocorrelation. In this method, the mean is

constant in the local neighborhood with minimum error variance (Lefohn et al., 2005).

Equation 5-3 (Yasrebi et al., 2009) used to estimate the predicted value using ordinary

kriging method.

Z(x)= ∑ 𝜆𝑖 𝑍𝑖𝑁𝑖=1 (5-3)

Where:

Z(x): The predicted value

Zi: The measured value at location i

𝛌: unknown weight for the measured value at the i location

N: The number of measured values.

Determine the phenomenon measurements can be costly, laborious and time

consuming. It is important to predict the values where the observations are not

available. Therefore, using interpolating technique is the solution to obtain the value of

the phenomenon at a location where data are not available using the sample point’s

data (Krivoruchko, 2012). There are two categories for the interpolation method

including: The deterministic methods and the probabilistic methods. Scouting for adult

Asian Citrus Psyllid are usually depends on visual counts or using sticky traps or sweep

82

net or using the tapping method. In this study a modified version of tap sampling

method was used for scouting Asian Citrus Psyllid adults and investigate the existing

infestation. The ultimate goal for this study is to utilizing the shaking machine for

monitoring Asian Citrus Psyllid in citrus groves and evaluate the accuracies of the

generated maps under different interpolation methods.

Material and Methods

Study Area

An experiment test was performed in citrus groves located at Citrus Research

and education center (CREC) in Lake Alfred, Florida located at latitude 28˚ 7' 50.78" N,

longitude 81˚ 43' 1.95" W (North-40 field) on May 2016. Weather conditions on study

area was with 25.62⁰C averaged temperature and 5.5 mph averaged wind speed. The

total area of the research plot used in this study was 0.655 ha (1.62 ac) s shown in

Figure 5-1. The citrus trees are Valencia and planted on 2012.


83

Insect Sampling Procedure and Data Collection

Zigzag Pattern Sampling:

A new sampling technique were implemented in the field to monitor ACP on May

2016. One of the common methods to monitor ACP in citrus trees is using yellow sticky

traps. A yellow sticky traps were used on specific location in the field to follow the

zigzag pattern to monitor ACP as shown in the Figure 5-2. The yellow sticky traps (0.20

by 0.26 m) were placed on the clipboard tool of the developed shaking machine to

capture the fallen psyllids adults during the scouting procedure. Two different data set

were employed in the field. The first data set was consisted from 42 locations was

distributed in the field that gives the zigzag pattern. The second data set consisted

from15 randomly selected trees for measuring ACP which represent as a reference

points to create the true population of ACP over the study area. In the Figure 5-2, each

symbol in the point layer represents a location where the ACP has been measured. The

ACP was collected by implement the mechanized tap method. The number of adult

psyllid captured on each sticky traps are counted manually.

Figure 5-2. Distribution of sample points representing the location of Asian Citrus Psyllid monitoring points.

987

654321

4241403938373635

3433323130

292827262524

232221201918

171615141312

1110

84

Sampling Technique

A new sampling technique was used which simulate the tap sampling method. In

this new technique, the developed shaking machine was used to simulate the tapping

method. The tractor-mounted limb shaker allowed the placement of the limb shaker to

be adjusted to the height of the limb using the hydraulic system of the tractor. The

shaker was operated between the rows outside the trees. The frequency of the shaker

was selected through the flow control valve in order to determine the speed of the

shaking arm. A frequency of (2.33 Hz) was selected based upon the results of a prior

field test conducted using this machine. Then, the operator positioned the shaker near

the endpoint of the selected limb. One of yellow sticky traps were placed on the

clipboard to catch the fallen psyllids as shown in Figure 5-3. Then, the shaking arm was

operated to shake the limb for a set period of time in the horizontal plane. A number of

psyllids fallen on to the sticky traps during shaking the limb. After each trial, the sticky

trap was warped with plastic wrap for laboratory counting. The number of adult psyllid

captured on each sticky traps are counted manually and then analyzed.

Figure 5-3. Developed shaking machine used for collecting Asian citrus psyllid in the field.

85

Recognizing Psyllids on Sticky Traps

One of the tasks of this experiment is to identify and count the psyllids in each

sticky trap. When traps were retrieved, they were covered with plastic wrap and

collected for counting of ACP in the laboratory. When evaluating the yellow sticky traps,

a magnifying glass was used to help recognize the psyllids from other insects and

debris on the card. Figure 5-4 shows the yellow sticky traps with (0.20 by 0.26 m) used

for this purposes.

Figure 5-4. Yellow sticky trap used for capturing Asian citrus psyllid.

86

Geostatistical Method for Interpolating Asian Citrus Psyllid Distribution.

The interpolation process was applied to the datasets using four different

interpolation techniques including: Inverse distance weighting, ordinary kriging, and

simple kriging to create the prediction map for psyllids. These methods were employed

to compare their performances for interpolating ACP distribution. Geostatistical

analyses were employed using the geostatistical analyst extension in ArcMap

(Arcgis10.3.1 © 1999-2015 Esri Inc. The measure of the accuracy, called the Root-

mean -squared error (RMSE) is one important measurements used for to test the

prediction map. The best interpolation method will be determined based on the smallest

root mean squared error obtained from each interpolation technique. Equation 5-1

shows the mathematical formula used for RMSE estimation (Odeh et al., 1994).

RMSE =√1

𝑛 ∑ (|𝑧(𝑥𝑖 ) − ��𝑛

𝑖=1 (𝑥𝑖 )|)2 (5-1)

Where

RMSE: Root-mean –squared-error

Z (Xi): the observed value at location i

��(Xi): the predicted value at location i

N: the sample size

Cross validation can be used to evaluate the results of interpolation techniques.

The cross validation is based on calculating the percent error (PE) (%) (Lu and Wong,

2008). The percent error can be calculated using equation (5-2) (Lu and Wong, 2008).

PE (%) = 𝑅𝑀𝑆𝐸

(1

𝑁) ∑ (𝑁

1 ��𝑖) × 100 (5-2)

87

Where:

PE: Percent error (%)

RMSE: Root-mean –squared-error

Pi: Observation value

N: Number of observations

Insect sampling, sample locations georeferenced, then by looking at the insect

count results, it is possible to correlate the insect population with spatial data. By using

Real time kinetic (RTK) GPS system, the location of each Ground Control Points (GPS)

is defined on a topographic map.

Result and Discussion

Exploratory Statistics for Asian Citrus Psyllid under Different Sampling Patterns with Different Traps Position

The summary statistics of ACP distribution using zigzag sampling patren with

horizontal traps position are shown in Table 5-1. The positive value of skewness and

kurtosis value is (0.838) and (2.924) respectively.

Table 5-1. Descriptive statistics for ACP distribution using zigzag sampling pattern. Sampling method Traps position Min Max Mean Std.dev Skewness Kurtosis Median

zigzag pattern Horizontal 0 9 2.738 2.479 0.838 2.924 2.5

Prediction of Asian Citrus Psyllid Distribution.

The number of adult psyllid captured on each sticky traps are counted manually

and then analyzed. The interpolation process was applied to the datasets using three

different interpolation methods including: Inverse Distance Weighting (IDW), Ordinary

kriging (OK) and simple kriging (SK) to estimate unknown ACP in the neighborhood.

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The results of the interpolation were represented over the study site map which shown

later.

Surface Mapping

By using the zigzag pattern of traps distribution in the field to capture Asian

Citrus Psyllid. The spatial distribution of Asian Citrus Psyllid produced by different

interpolation methods from samples taken at 42 locations are shown in the Figures (5-5,

5-6 and 5-7) which is due to many factors. Each prediction map provide insect

population distribution represented by a specific color on the map which is shown in the

accompanying key. The minimum value (zero) represents the lowest number of psyllids

and the maximum value (9) represents the maximum value of psyllids in three

interpolation techniques. The possible explanations for the high infestation with adult

psyllids in certain areas of the field is the new flush and the temperature which effects

on the psyllids population (Rogers and Stansly ,2006) and psyllid population correlated

positively with the relative humidity and the emergence of new leaves (Martini et

al.,2016). Furthermore, the edges of the field have a strong effect on the psyllid

distribution (Setamou and Bartels, 2015)

Measures of Accuracy of Prediction Maps

The prediction error mean and the root mean squared error can be seen in the

Figure 5-8 and Figure 5-9 for different interpolation techniques and sumrazied in Table

5-2 .Figure 5-8 shows that the mean is ( = 0.178) for Inverse weighting distance and

RMSE= 2.736, and for the ordinary kriging interpolation , mean =0.246 and RMSE =

2.925. For the simple kriging, the mean = 0.046 and RMSE =2.651.

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Figure 5-5. Prediction map of Asian citrus psyllids using inverse distance weighting

technique.

Figure 5-6. Prediction map of Asian citrus psyllids using ordinary kriging technique.

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Figure 5-7. Prediction map of Asian citrus psyllids using simple kriging technique.

The performance of the methods was assessed using two indices which

calculated from the measured and interpolated values at each selected location for data

set. The accuracy was measured by the root mean squared error (RMSE) and Percent

Error (PE) for 15 validation data set selected as shown in Table 5-3. As interpreted from

Table 5-3, RMSE and PE (%) for different interpolation techniques are not the same.

From Table 5-3, there is no differences between ordinary kriging and simple kriging

since the results was the same in terms of RMSE and PE. it can be concluded from the

Table 5-3 that Inverse weighting distance (IDW) is more accurate for predicting the

spatial distribution of Asian citrus psyllid than other methods since RMSE= 2.708 and

PE = 0.864 which they are the lowest values compared to other methods. Figure 5-10

support that IDW method performs better than OK and SK methods for prediction since

percent error was the lowest value using IDW (=86.42).

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Table 5-2. The means of mean (M) and root mean square error (RMSE) for different interpolation methods.

Interpolation methods

Prediction error

M RMSE

Inverse distance weighting 0.178 2.736


Simple kriging 0.046 2.651

Table 5-3. RMSE, Error (%) and RMSE for different interpolation methods.

Interpolation technique PE (%) RMSE

Inverse Distance Weighting 86.42 2.708 Ordinary Kriging 96.45 3.022 Simple Kriging 96.09 3.011

Figure 5-8. The cross validation comparison of the ACP distribution map between inverse distance weighting and simple kriging method.

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Figure 5-9. The cross validation comparison of the ACP distribution map between inverse weighting distance and ordinary kriging.

Figure 5-10. The percent error with different interpolation methods for ACP prediction.

The conclusions of this study can be summarized as follow:

This study aimed to implement shaking machine for monitoring Asian Citrus

Psyllid and compare different interpolation methods for generating surface maps of

86.42

96.45 96.09

80

82

84

86

88

90

92

94

96

98

IDW OK SK

Err

or

(%0

Interpolation methods

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Asian Citrus Psyllid. The data set consisted of 42 selected tree location in a zigzag

pattern as long as 15 tree locations was selected as reference points where the number

of ACP were measured. IDW, OK and SK interpolation techniques were used in

producing the surface maps for ACP density over the study area. The accuracy of the

prediction maps was measured using three measurement indices. The results obtained

from inverse distance weighting, ordinary kriging interpolation and simple kriging show

clearly the inverse distance weighting is better than the other interpolation methods

since its provide more accurate prediction than other interpolation methods.

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CHAPTER 6 ASIAN CITRUS PSYLLID MONITORING CALCULATIONS

The advantage of the developed shaking machine as an alternative method for

ACP monitoring is to reduce time and labor cost. In this section, the focus will be on

operation cost and its effects on the total labor cost for the farmer.

The shaking machine is designed to reduce the sampling cost in the field. In

terms of labor, implementing the shaking machine will allow the field to be monitored

with fewer workers. The largest benefit of using the shaking machine for ACP sampling

is to improve work efficiency by reducing technician work time in the field.

Field Capacity and Efficiency

The field capacity of farm machinery is defined as the rate of the work per hour

and is mostly often measured in acres per hour of operation (Hanna, 2016). Theoretical

field capacity (TFC) is based on using the full width and the travel speed of the

machine. The unit of TFC are in acres per hour and can be expressed as:

TFC = Width (ft)×speed (

mi

hr)

8.25

(6-1)

The effective field capacity is the ability of the machine to do the function under

field conditions and it is always less than the theoretical field efficiency due to many

factors such as machine adjustment, lubrication and refueling during the day, repairs,

and turning…etc. Field efficiency (FE) is the ratio of the actual field capacity (EFC) to

the theoretical field capacity (TFC) (Hanna, 2016). Field efficiency is not constant due to

many factors including: size and shape of the field, crop yield, crop condition, moisture

and the pattern of field operation

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FE (%)=EFC

TFC × 100

(6-2)

Comparison of the Labor Cost Using the Conventional Tap Sampling Method Versus the Shaking Machine Method

An algorithm was developed in MATLAB to compute the time required for

sampling the ACP for both the conventional tap sampling method and the shaking

machine method.

It can be seen from Figure 6-1 that utilizing the shaking machine system required

less time than the conventional tap sampling method to accomplish a task. Hours of

labor using tap method exceeded that of the shaking machine by 160 percent because

travel speed is lower and the time required for sampling is higher for the tap method

than that of the shaking machine. The shaking machine runs at an average speed of 10

mph and 0.91 min, i.e. the total time for monitoring ACP, while the tap method has an

average travel speed of 1.78 mph and 1.88 min to monitor ACP. From the Figure 6-1,

the shaking machine required 45 minutes (less than an hour) for ACP sampling, while

for tap method required approximately 117 min for sampling ACP for the same size area

and with the zigzag pattern. Consequently, labor cost can be calculated by multiplying

the labor wage rate times the number of hours. Since the labor cost is relatively fixed, t

the total cost can be determined. Then, by multiplying the labor cost by the hours

required to finish the work, the cost can be determined for both methods. Currently, a

Florida grower pays ($15.00) per hour for an operator to perform the tap method in the

field.

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It can be concluded that the labor cost for the shaking machine for the study area

using the zigzag pattern to monitor 42 trees is approximately $15, whereas using the

tap method for the same area is $30.

The calculations listed above did not include machine preparation, travel time to

and from the field, turning, and other delays. Also, for the tap method, the operator

brakes time is not included in the calculations.

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Figure 6-1. Comparison of two different methods on the total time required to monitor Asian citrus psyllid in citrus groves.

The Blue line represents the tap sampling method and red line represents shaking machine method.

98

CHAPTER 7 CONCLUSIONS AND FUTURE WORK

Conclusions

The main objectives of this study were to build a shaking machine for quick and

cost- effective monitoring and mapping the ACP distribution in citrus groves. Work

involved building a prototype of a mechanical limb shaking machine and then utilizing it

in citrus groves. The machine was developed and fabricated in the workshop of the

University of Florida, Citrus Research and Education Center (CREC) in Lake Alfred,

Florida.

Besides building the shaking machine, four main experiments were conducted in

the field. The first experiment was conducted in order to develop sampling techniques in

terms of optimal placement (horizontal, and vertical) for the colored sticky traps that

monitor ACP using different sampling patterns (grid and zigzag), generating geo-insect

prediction maps in citrus groves in Florida.

The second and third experiments were conducted to determine the relationship

between striking the branches and acceleration. A total of 54 samples were randomly

selected in a citrus grove in Lake Alfred to accomplish the objectives of both studies. In

the tap sampling method experiment, a modified version of the tap sampling method of

Stansly et al., (2010) was performed with the exception that no insects were collected.

Accelerometer sensors were placed on the limb in order to sense and report the

acceleration. A rod was used to strike the branch for a fixed length of time (5, 10, and

15 s) for different limb lengths. The results showed that the effect of limb length and

shaking time on the amount of the acceleration was not significant at the 0.05

significance level.

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To find the optimal system operation, an experiment was conducted with three

shaking frequencies of (0.26, 2.33 and 4.0 Hz) and three shaking durations (5, 10, and

15s). Accelerometer sensors were placed on the limb to sense and report the

acceleration. The accelerometer sensors were attached to a programmed Arduino UNO

R3 board as data logger which is attached via a USB port on a laptop to record the data

using open source Cool Term software (Version 1.4.6). The data collected using the

data logger then were corrected by removing the unwanted buffered data from the

beginning and the end part of the data package. Then, a code in MATLAB 2016a

software was used to produce the resultant from raw data, and visualize and analyze

the processed data. The results show that there are significant differences for the

shaking duration and shaking frequency at the 0.05 level of significance.

The fourth experiment objectives were to utilize the shaking machine to monitor

ACP in citrus groves and evaluate the accuracies of the generated maps under different

interpolation methods. IDW, OK and SK interpolation techniques were used in

producing the prediction maps for ACP over the study area. Two indices were used to

assess the performance of the interpolation methods: root -mean -squared error

(RMSE) and percent error (PE). The map from IDW method is the accurate than the

other two interpolation methods since their high accuracy of prediction map.

Future Work

improvement of shaking machine can be reached by reducing the time required

for ACP sampling in the citrus groves. This step can be achieved through developing an

image processing system to count ACP attached to the sticky traps. This can be done

using a high resolution digital camera that enable taking a high-resolution image of

sticky traps during ACP sampling. These digital images will provide a quick identification

100

of insect infestation. This will also improve economic efficiency in the future which help

to reduce labor time, enable a large amount of the samples to be processed, and

provide consistent output and cost savings due to the improvement in the work

efficiency of the operator especially for the large area.

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APPENDIX MATLAB CODES

The following function was used to determine the total time required for

monitoring Asian citrus psyllid using tap method and shaking machine method.

clear close all clc

%% distance =

[0;74.06;76.27;77.39;37.31;39.29;38.49;74.21;74.21;76.59;36.90;10.32;36.11;38

.89;75;36.11;78.18;31.74;37.31;44.44;69.45;40.48;74.22;16.31;36.11;76.59;33.3

4;37.31;80.18;26.20;74.22;37.70;37.31;79.77;23.44;37.30;40.88;40.08;33.75;36.

91;40.48;36.57]; % m machin_speed = 268.22; % m/min time_move_machin = distance/machin_speed; tap_speed = 48; % m/min time_move_tap = distance/tap_speed;

tap = 1.88; machin = 0.91; tap_distance_time = 0; machin_distance_time =0;

for n = 1:42 machin_time(n,1) = n*machin; tap_time(n,1) = n*tap; if (n>1) machin_distance_time(n,1) =

machin+time_move_machin(n,1)+machin_distance_time(n-1,1); tap_distance_time(n,1) = tap+time_move_tap(n,1)+tap_distance_time(n-

1,1); end end

figure(1) hold on box on plot(tap_distance_time) plot(machin_distance_time) ylabel('Time(minutes)') xlabel('Tree number') legend('Tap method','Machine Method')

The following function was used to determine the resultant acceleration for tap

method and shaking machine method.

102

%% Load data Filename = 'data.txt'; Delimiter = ','; startRow = 2; formatSpec = '%f%f%f%f%f%f%f%f%f%f%f%f%f%[^\n\r]'; fileID = fopen(filename,'r'); dataArray = textscan(fileID, formatSpec, 'Delimiter', delimiter, 'HeaderLines' ,startRow-1, 'ReturnOnError', false); fclose(fileID); t_ms = dataArray{:, 1}/1000; sensor1.x = dataArray{:,2}-mean(dataArray{:,2}); sensor1.y = dataArray{:,3}-mean(dataArray{:,3}); clearvars filename startRow delimiter formatSpec fileID dataArray ans; %% Plot data figure('Name','Cartesian (x,y,z) Coordinates') % x data subplot(3,1,1) hold on plot(t_ms,sensor1.x,'color',[1,0.3,0]) xlim([0,max(t_ms)]) xlabel('Time, [s]') ylim([-6,6]) ylabel('a_{x}, [g]') title('(a)') box on grid on legend('S_{1}','S_{2}','S_{3}','S_{4}','Location','northeastoutside') % y data subplot(3,1,2) hold on plot(t_ms,sensor1.y,'color',[1,0.3,0]) xlim([0,max(t_ms)]) xlabel('Time, [s]') ylim([-6,6]) ylabel('a_{y}, [g]') title('(b)') box on grid on legend('S_{1}','S_{2}','S_{3}','S_{4}','Location','northeastoutside') % z data subplot(3,1,3) hold on plot(t_ms,sensor1.z,'color',[1,0.3,0]) xlim([0,max(t_ms)]) xlabel('Time, [s]') ylim([-6,6])

103

ylabel('a_{z}, [g]') title('(c)') box on grid on legend('S_{1}','S_{2}','S_{3}','S_{4}','Location','northeastoutside') %% Plot data figure('Name','Spherical (r,theta,phi) Coordinates') % r data for i=1:size(t_ms,1) sensor1.r(i,1)=sqrt((sensor1.x(i,1)^2)+(sensor1.y(i,1)^2)+(sensor1.z(i,1)^2)); end subplot(3,1,1) hold on plot(t_ms,sensor1.r,'color',[1,0.3,0]) xlim([0,max(t_ms)]) xlabel('Time, [s]') ylim([0,10]) ylabel('a_{r}, [g]') title('(a)') box on grid on legend('S_{1}','S_{2}','S_{3}','S_{4}','Location','northeastoutside') % theta data for i=1:size(t_ms,1) sensor1.theta(i,1)=atan((sensor1.y(i,1)^2)/(sensor1.x(i,1)^2))*(182/pi); end subplot(3,1,2) hold on plot(t_ms,sensor1.theta,'color',[1,0.3,0]) xlim([0,max(t_ms)]) xlabel('Time, [s]') ylim([-180,180]) ylabel('a_{\theta}, [deg]') title('(b)') box on grid on legend('S_{1}','S_{2}','S_{3}','S_{4}','Location','northeastoutside') % phi data for i=1:size(t_ms,1) sensor1.phi(i,1)=atan(sqrt((sensor1.x(i,1)^2)+(sensor1.y(i,1)^2))/(sensor1.z(i,1)))*(182/pi); end subplot(3,1,3) hold on plot(t_ms,sensor1.phi,'color',[1,0.3,0])

104

xlim([0,max(t_ms)]) xlabel('Time, [s]') ylim([-180,180]) ylabel('a_{\phi}, [deg]') title('(b)') box on grid on legend('S_{1}','S_{2}','S_{3}','S_{4}','Location','northeastoutside') %% Max and Min Data disp('x1(min,max); y1(min,max); z1(min,max); r1(max)') disp(['x1(',num2str(min(sensor1.x)),',',num2str(max(sensor1.x)),'); y1(',num2str(min(sensor1.y)),',',num2str(max(sensor1.y)),'); z1(',num2str(min(sensor1.z)),',',num2str(max(sensor1.z)),'); r1(',num2str(max(sensor1.r)),')']) disp('***************') disp('x2(min,max); y2(min,max); z2(min,max); r2(max)') %% Dectct Peaks of r>=1g k(1) = 1; k(2) = 1; k(3) = 1; k(4) = 1; for i=5:size(t_ms,1)-5 if and(gt(sensor1.r(i,1),sensor1.r(i-1,1)),gt(sensor1.r(i,1),sensor1.r(i+1,1))) if ge(sensor1.r(i,1),1) sensor1.r_peaks(k(1),1)=sensor1.r(i,1); k(1) = k(1)+1; end end figure('Name','Boxplot of peaks for r') subplot(1,4,1) boxplot(sensor1.r_peaks,'labels',{'Sensor 1'}) ylabel('ar Peaks, (g)')

105

LIST OF REFERENCES

Atakan E, Canhilal R. (2004). Evaluation of yellow sticky traps at various heights for Monitoring cotton insect pests. J. Agric. Urban Entomol. 21:15–24.

Aubert, B. (1990a). Integrated activities for the control of Hunglungbin-greening and its Diaphorina citi Kuwayama in Asia.pp.133-144 in B. Aubert, S Tontyaporn, and D. Buangsuwon [eds.], Rehabilitation of Citrus Industry in the Asia Pacific Region. Proc. Asia Pacific International Conference on Citriculture, Asia Pacific Region. Chiang Mai, Thailand, 4-10 February 1990.

Coates, W.E. (2002). Agricultural machinery management. The University of Arizona. Cooperative extension.

ESRI. (2016). What is the Empirical Bayesian kriging? Available at: http://desktop.arcgis.com/en/arcmap/latest/extensions/geostatistical -analyst/what is –empirical –Bayesian-kriging-html.

Freescale Semiconductor. (2008). Technical data. Document number: Available at: MMA7368L.http://www.freescale.com.

Gencsoylu, I. (2007). Evaluation of yellow sticky trap on population of some cotton pests. AM. EURAS.J. Agric.Environ, Sci., 2:62-67.

Gomez, H. (2009). Screening Aid for Huanglongbing (HLB) or Citrus Greening Disease Symptoms. Plant Pathologist, USDA, APHIS. PPQ CHRP Program.

Gotway, C.A., Ferguson, R.B., Hergert, G.W.and Peterson, T.A. (1996). Comparison of

kriging and inverse-distance methods for mapping soil parameters. Soil Sci.Soc. Am. J., 60 (1996), pp. 1237–1247

Grafton-Cardwell, B. (2016). Sampling and management of Asian Citrus Psyllid. Dept. of. Entomology, UC Riverside. Available at: https://www.sdfarmbureau.org/ACP/ACP-SamplingManagemen%20Plans.pdf Entomology

Grafton-Cardwell, E.E. and Daugherty, M.P. (2013). Asian Citrus Psyllid and

Huanglongbing disease. University of California. Publication 74155.

Grafton-Cardwell, E, E. Godfrey K, Rogers M, Childers C, Stansly P. (2006). Asian citrus psyllid. University of California. ANR Publications 8205.

Gruver, A. and Dutton, J.A. (2014). Inverse Distance Weighting. E-Education Institute, college of Earth and Mineral Sciences, the Pennsylvania State University. https://www.e-education.psu.edu/geog486/node/1877

Gutierrez, A.P. and Ponti, L. (2013). Prospective analysis of the geographic distribution and relative abundance of Asian Citrus psyllid (hemiptera: liviidae) and citrus

http://www.freescale.com/

https://www.sdfarmbureau.org/ACP/ACP-SamplingManagemen%20Plans.pdf

https://www.e-education.psu.edu/geog486/node/1877

106

Greening disease in North America and the Mediterranean basin. Florida Entomologist 96(4).

Halbert, S.E.and Manjunath, K.L. (2004). Asian citrus psyllids (Sternorrhycha: Psyllidae) and greening disease of citrus: a literature review and assessment of risk in Florida. Fla. Entomol. 87 (3) (2004), pp. 330–353.

Hall, D.G.and Hentz, M.G. (2010). Sticky trap and stem–tap sampling protocols for the Asian citrus psyllid (Hemiptera: Psyllidae). J. Econ. Entomol. 103:541–49.

Hall, D.G., Sétamou, M., and Mizell, R.F. (2010b) A comparison of sticky traps for monitoring Asian citrus psyllid (Diaphorina citri Kuwayama). Crop Protection 29: 1341–1346.

Hall, D.G, Hentz, M.G and Ciomperlik, M.A. (2007). A comparison of traps and stem tap sampling for monitoring adult Asian citrus psyllid (Hemiptera: Psyllidae) in Citrus. Fla. Entomol. 90:327–34.

Hanna, M. (2016). Estimating the field capacity of farm machines. Ag Decision Maker. File A3-24.

Hodges, A.W, Spreen, T.H. (2012). Economic impacts of citrus greening (HLB) in Florida, 2006/07–2010/11. University of Florida, Institute of Food and Agricultural Science: Gainesville, FL, USA, 2012. EDIS document FE903.

Karydas.C. G, Gitas, L.Z., Koutsogiannak, E., Lydakis-Simantiris, N. and Slleos, G.N. (2009). Evaluation of spatial interpolation techniques for mapping agricultural topsoil properties in Crete. EARSeL eProc. 8: 26–39.

Kemp, J.G. and Melanson, B.A. 1977. Mobile limb shaker for apple harvesting. Can. Agric. Eng., 19 (1977), pp. 88–91

Krivoruchko, K. (2012). Empirical bayesian kriging implemented in Arc GIS geostatistical analyst. Available at: WWW.esri.com. http://www.esri.com/news/arcuser/1012/empirical-Byesian-kriging.html.

Lefohn, Allen S., Knudsen, H. Peter; and Shadwick, Douglas S. (2005). Using Ordinary Kriging to Estimate the Seasonal W126, and N100 24-h Concentrations for the Year 2000 and 2003. A.S.L. & Associates, 111 North Last Chance Gulch Suite 4A Helena, Montana 59601.

Leone, A., Romaniello, R., Tamborrino, A., Catalano, P., Peri, G. Identification of vibration frequency, acceleration, and duration for efficient olive harvesting using a trunk shaker. Trans ASABE, 58 (2015), pp. 19–26

Leigh, T.F., Maggi, V.L. and Wilson, L.T. (1984). Development and use of machine for the recovery of Arthropods from plant leaves. J. Econo.Entomol. 77:271-276.

http://www.esri.com/news/arcuser/1012/empirical-byesian-kriging.html

107

Liu, Y.H. and Tsai, J.H. (2000). Effects of temperature on biology and life table parameters of the Asian citrus psyllid, Diaphorina citri Kuwayama (Homoptera: Psyllidae). Ann. Applied Biol 137:201–216.

Lu, G.Y. and Wong, D.W. (2008). An adaptive inverse -distance weighting spatial interpolation technique. Comput.Geosci.,34,1044-1055.

Miranda, J.L., Gerardo ,B.D.and Tanguilig III,B.T. (2014). Pest Detection and Extraction Using Image Processing Techniques. International Journal of Computer and Communication Engineering 3.3: 189-192.

Martini, X., Pelz-Stelinski, K. S and Stelinski, L.L. (2016). Factors Affecting the Overwintering Abundance of the Asian Citrus Psyllid (Hemiptera: Liviidae) in Florida Citrus (Sapindales: Rutaceae) Orchards. Florida Entomologist 99(2):178-186.

Martini, X., Seibert, S, Prager, S.M. and Nansen, C. (2012). Sampling and interpretation of psyllid nymph counts in potatoes. Entomologia Experimentalis et Applicata. 143: 103–110.DOI: 10.1111/j.1570-7458.2012. 01235.x

Majumdar, A and Fadamiro, H. (2009). Asian Citrus Psyllid. Albama Cooperative Extension System. ANR-1341.

Mensah, R.K. (1996) Evaluation of colored sticky traps for monitoring populations of Austroasca vividigrisea (Paoli) (Hemiptera: Cicadellidae) on cotton farms. Australian Journal of Entomology, 35, 349–353.

Meyer, J.R. (2007). Impact of Insect. Department of Entomology. NC state university. https: //projects.ncsu.edu/cals/course/ent425/text01/impact1.html.

Mead, F.W.and Fasulo, T.R. (1998). Asian Citrus psyllid. Florida department of Agriculture and consumer services, division of plant industry and University of Florida. http://entnemdept.ufl.edu/creatures/citrus/acpsyllid.htm.

Majumdar, A., Nesbitt, N.and Fadamiro, H. (April 2009) Asian Citrus Psyllid. Alabama Cooperative Extension System.

Mishra, A.R.; Karimi, D.; Ehsani, R.; Lee, W.S. (2012). Identification of citrus greening (HLB)Hemiptera: Psyllidae). Florida department of agriculture and consumer services, division using a Vis-NIR spectroscopy technique. Trans. ASABE, 55, 711–720.

Meyer, J.R. (2007). A class of distinction. Department of entomology. NC State University. http://www.cals.ncsu.edu/course/ent425/text01/impact1.html.

Monzo, C., Arevalo, H.A., Jones, M.M., Vanaclocha, P., Croxton, S.D., Qureshi,J.A. and Stansly, P.A. (2015). Sampling Methods for Detection and Monitoring of the Asian Citrus Psyllid (Hemiptera: Psyllidae). Environ Entomol. 44(3):780-8.

https://projects.ncsu.edu/cals/course/ent425/text01/impact1.html

http://entnemdept.ufl.edu/creatures/citrus/acpsyllid.htm

http://www.cals.ncsu.edu/course/ent425/text01/impact1.html

108

Nishida, T. and Takara.J.(1979). Two simple insect sampling device. Proc. Hawaii. Entomol.Soc. 23(1): 129-133.

Odeh, I.O.A, Mcbratney, A.B, Chittleborough, D. (1994) I. Odeh, A. McBratney, D. Chittleborough. Spatial prediction of soil properties from landform attributes derived from a digital elevation model Geoderma, 63 (3–4) (1994), pp.197–214.

Pelz-Stelinski, L., Rogers, M., Yates, J., Dewdney, M., and Spann, T. (2009). Low volume applications. An Emerging Technology in the Citrus Industry. University of Florida. IFAS extension.

Pelz-Stelinski KS, Brlansky RH, Ebert TA, Rogers ME. (2010). Transmission parameters for Candidatus Liberibacter asiaticus by Asian citrus psyllid (Hemiptera: Psyllidae). J. Econ. Entomol. 103:1531–41

Premalatha, K and Rajangam, J. (2011). Efficacy of yellow sticky traps against greenhouse whitefly, Trialeurodes vaporariorum (Westwood) (Aleyrodidae: Hemiptera) in gerbera. Journal of Biopesticides, 4 (2): 208-210.

Quarles, W. (2013). IPM for Asian citrus psyllids and Huanglongbing disease. IPM practitioner. The IPM practitioner. Volume xxxiv. Number ½.

Rogers, M.E., and Stansly, P.A. (2006). Biology and management of the Asian citrus psyllid, Diaphorina citri Kuwayama, in Florida citrus. Publication#ENY- 739. University of Florida. IFAS Extension.

Schnelle, M. and Rebek, E. (2016). IPM-Scouting and monitoring for pests in commercial green houses. Oklahoma cooperative extension service. HLA-6711.

Setamou, M. and Bartels, D.W. (2015). Living on the Edges: Spatial Niche Occupation of Asian Citrus Psyllid, Diaphorina citri Kuwayama (Hemiptera: Liviidae), in Citrus Groves. Journal. Pone. 0131917.

Sharma, A.K.2005. Text book of correlation and regression. Discovery published house.

Stansly, P., Arevalo, A., and Qureshi, J. (2010). Monitoring methods for Asian Citrus Psyllid. Citrus industry. April 2010.

Snieder, R. (2004). A guided tour of mathematical methods: For the physical science. Department of geophysics and center for wave phenomena, Colorado school of Mines.

Sumner, H.R.1973. Selective harvesting of Valencia orange with a vertical canopy shaker. Transaction by the American Society of Agricultural Engineers. 16-6:1024-1025.

Thomas, D.B. (2012) Comparison of insect vacuums for sampling Asian citrus psyllid (Homoptera: Psyllidae) on citrus trees. Southwestern Entomologist 37: 55–60.

109

Thangalakshmi, S. and Ramanujan, R. (2015). Electronic trapping and monitoring of insect pests troubling agricultural fields. International Journal of Emerging Engineering. Research and Technology. Volume 3, Issue 8. ISSN 2349-4395.

Teck, S.L.C., Fatimah, A., Beattie, A., Heng, R.K.J. and King, W.S. (2011). Seasonal population Dynamics of the Asian Citrus Psyllid, Diaphorina citri Kuwayama in Sarawak. American Journal of Agricultural and Biological Sciences 6 (4): 527-535.

Tsai, J.H., Wang, J.J. and Liu, Y.H. (2002). Seasonal abundance of the Asian citrus psyllid, diaphorina citri (homoptera: psyllidae) in southern Florida. Florida Entomologist. 85:446-451.

USDA /NASS. (2016). Citrus Statistics 2014-2015.Retrived from the United States Department of Agriculture Available at: https://www.nass.usda.gov/Statistics_by_State/Florida/Publications/Citrus.

USDA/APHIS. (2010). Technical Working Group Report. National Surveillance strategies for Asian Citrus Psyllid, (Diaphorina citri) and Huanglongbing (Associated with Candidatus Liberibacter spp.). Retrieved from USDA’s Marketing and Regulatory Programs agency. Available at: https://www.aphis.usda.gov/plant_pest_info/citrus_greening/downloads/pdf

Yasrebi, J., Saffari, M., Fathi, H., Karimian, N., Moazallahi, M., Gazni, R. (2009). Evaluation and comparison of ordinary kriging and inverse distance weighting methods for prediction of spatial variability of some soil chemical parameters.Res. J. Biol. Sci., 4 (2009), pp. 93–102.

Yen A.L., Madge D.G., Berry N.A., Yen J.D.L. (2012). Evaluating the effectiveness of five sampling methods for detection of the tomato potato psyllid, Bactericera cockerelli (Šulc) (Hemiptera: Psylloidea: Triozidae). Aust.J. Entomol. 52:168– 174.

Zacharda, M., Pultar, O. and Muska, J. (1998). Washing Technique for Monitoring Mites in Apple Orchards. Experimental & Applied Acarology, 5: 181-183.

Zehnder, G. (2014). Overview of monitoring and identification techniques for insect pest. Clemson University. Available at: http://www.extension.org/pages/19198/overview-of- Monitoring-and-Identification-techniques-for-insect-pests.

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BIOGRAPHICAL SKETCH

Muna Jamil Abbas was born and raised in Baghdad, Iraq. She received her

Bachelor of Science in Agricultural Mechanization from college of Agriculture, University

of Baghdad in Iraqi in 1993. In 2004, she received her Master degree in Science in

Agricultural Mechanization from college of Agriculture, University of Baghdad.

Muna received award from the president of University of Baghdad since she

ranked the first over College of Agriculture when she received her Master degree. In

Fall 2010, she attended University of Florida to pursue a doctoral degree at Agricultural

and Biological Engineering department. In 2014, she received her second Master of

Science degree in Agricultural and Biological Engineering, University of Florida. She

was awarded a PhD degree in the Agricultural and Biological Engineering from

University of Florida in 2017.

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