Understanding the Helicoverpa Armigera Pest Population Dynamics related to the chickpea crop using

Neural Networks

Rajat Gupta, B Narayana, Krishna Polepalli, G. Ranga Rao, C Gowda, Y. Reddy and G.Rama Murthy

INDEX Introduction Objective Motivation Pest Dynamics Models Developed in the Past Why they Failed ? Preliminaries

Dataset Description Results

Mean Graphs Majority Voting

Conclusion

Introduction

Helicoverpa Armigera Chickpea Crop

Participating Organizations

International Institute of Information Technology

(IIIT)

International Crop Research for the Semi-Arid Tropics

(ICRISAT)

Objective To develop a pest forecasting mechanism

by extracting pest dynamics from Pest surveillance database using Knowledge Discovery and Data Mining techniques.

To understand the interaction of various factors responsible for pest outbreaks.

Motivation Insect pests are the major cause of crop

loss. The crop loss due to lack of advance

information about pest emergence often leads to financial bankruptcy of the farmers.

Pest Dynamics Highly dynamic nature of the Pest

Ability to adapt to new conditions quickly Can migrate to long distances Hibernate when condition are not favorable Feeds on wide variety of hosts

Models Developed in the Past Techniques used were essentially Statistical

(Correlation and Regression Analysis) T.P. Trivedi had proposed a regression model to

predict the pest attack. Model seems to work only for some years (1992-1994)

Correlation analysis was used by C.P. Srivastava to explore the relationship between the rainfall and pest abundance in different years. The technique is not effective as the attributes don’t follow

normal distribution

Why they FAILED?

Techniques used are able to capture only linear relationships.

Problems with the dataset (noisy data) All events are treated equally

Pest Surveillance Dataset Helicoverpa armigera pest data on

Chickpea crop provided by International Institute for Semi-Arid Tropics (ICRISAT).

The dataset spans over a period of 11 years (1991-2001).

It contains information on 17 attributes.

Dataset Description These Dataset contains 17 attributes which

can be classified as Weather parameters Pest Incidence Farm Parameters

Weather parameters Rainfall Relative Humidity Minimum Temperature Maximum Temperature Sunshine hours.

Pest Incidence Eggs/Plant Larvae/Plant Light Trap Catch Pheromone Trap Catch

Farm Parameters Zone Location Area Surveyed Plant Protection User Season

Neural Networks A Neural Network is an interconnected

assembly of simple processing elements, units or nodes, called neurons.

The processing ability of the network is stored in the inter-unit connection strengths or weights.

Learns from a set of training patterns.

Multi Layer Neural Networks

Inputs Outputs

Hidden Layer

Why Neural Networks ? Neural Networks don’t make any distributional

assumption about the data. It learns the patterns in the data, while statistical

techniques try to do model fitting.

This makes neural network modeling a powerful tool for exploring complex, nonlinear biological problems like pest incidence.

Data Preprocessing Data Selection Data Reduction Null Values Data Transformation

Normalization Fourier Transform

Neural Network Training Dataset

Advance Dataset (X) where X =0,12,3. Training Dataset - 8 years (1991 - 1998) Test Dataset - 3 years (1998 - 2001)

Learning Algorithm – Levenberg-Marquardt. Bayesian Regularization Hyperbolic Tangent Sigmoid function in hidden

layers (2 hidden layers) Linear Transfer function in outer layer

Datasets Generated Advance (0) Advance (1) Advance (2) Advance (3)

Average R-valueDataset Average R-value

(for 15 models)

Advance(0) 0.91

Advance(1) 0.96

Advance(2) 0.91

Advance(3) 0.75

Larvae/Plant -Advance(0)

Larvae/Plant -Advance(1)Larvae/Plant -Advance(1)

Larvae/Plant -Advance(2)Larvae/Plant -Advance(2)

Larvae/Plant -Advance(3)Larvae/Plant -Advance(3)

Majority Voting(40%)

# Hits # Miss # False Alarm

Advance(0) 27 4 6

Advance(1) 29 1 4

Advance(2) 27 2 12

Advance(3) 22 6 15

Majority Voting(50%)

# Hits # Miss # False Alarm

Advance(0) 27 4 6

Advance(1) 28 2 2

Advance(2) 26 3 11

Advance(3) 22 6 12

Majority Voting(60%)

# Hits # Miss # False Alarm

Advance(0) 25 6 6

Advance(1) 26 4 2

Advance(2) 26 3 11

Advance(3) 21 5 12

Conclusion We can now predict the pest attack using

Neural Networks two weeks in advance with high probability.

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