Image processing and Machine learning for Automated fruit grading system –A Review

Guided by : Prof.Nikunj Gamit Prepared By: Rashmi Pandey

• Image Processing in Agriculture

• Image processing and analysis

• Fruit Grading Process

• Literature review based on color feature extraction techniques

• Workflow

• Future Work

Outline of Presentation

Image Processing in Agriculture[1]

• Detection of disease in leaf, stem fruits and vegetables.

• Determination of shape and color characterization of fruits.

• In remote sensing and irrigation.

• In fact, quantification of the visual properties of

horticultural products and plants can play an important role

to improve and automate agricultural management tasks.

Image Processing and analysis[2]

Fruit Grading Process[3]

Extracted features from processed image

Parameters Processing

Color Color value and degree of color distribution are

Measured based on R, G, and B color

component ratio.

Shape Shape is measured as boundary-based

features, region-based features, mathematical

morphology, and so on.

Size Size is measured from the maximum length or area

on upper image or calculated volume from several

images.

B Back

Paper Title Year Author Published In Method

Automated strawberry grading system based on image processing

2010 Xu Liming, Zhao Yanchao

Computers and Electronics in Agriculture, Elsevier

Dominant Colour Method

Computer vision based date fruit grading system: Design and implementation

2011 Yousef Al Ohali Journal of King Saud University – Computer and Information Sciences, Elsevier

Intensity Distribution Method

Mango Grading By Using Fuzzy Image Analysis

2012 Tajul Rosli B. Razak, Mahmod B. Othman, Mohd Nazari bin Abu Bakar, Khairul Adilah bt Ahmad, Ab Razak Mansor

International Conference on Agricultural, Environment and Biological Sciences

Mean of colour in images

Literature Survey

Paper Title Year Author Published In Method

Application of neural networks to the color grading of apples

1997 Kazuhiro Nalcano Computers and Electronics in Agriculture, Elsevier

Nine Color Characteristic Data

Development of a lemon sorting system based on color and size

2010 M. Khojastehnazhand, M. Omid and A. Tabatabaeefar

African Journal of Plant Science HSI Colour Model Technique

Adaptive texture and color segmentation for tracking moving objects

2002 Ercan Ozyildiz, Nils Krahnst-over, Rajeev Sharma

Pattern Recognition, Elsevier YES Colour Model Technique

Literature Survey

Paper Title Year Author Published In Method

Automated Grading of Palm Oil Fresh Fruit Bunches (FFB) using Neuro-Fuzzy Technique

2009 Nursuriati Jamil, Azlinah Mohamed and Syazwani Abdullah

International Conference of Soft Computing and Pattern Recognition

Simple RGB model

A practical solution for ripe tomato recognition and localization.

2013 Xuming Chen and Simon X. Yang

Journal of Real-Time Image Processing

The Segmentation Method

Rapid Color Grading for Fruit Quality Evaluation Using Direct Color Mapping

2011 Dah-Jye Lee, James K. Archibald and Guangming Xiong

IEEE transactions on automation science and engineering

Direct Color Mapping Technique

Post-harvest profile of mango

2013 A Report

Literature Survey

Input image

Segmentation

Classification (FL, NN or

SVM)

Grade I Grade II Grade III

Implementation Work Flow

Colour Feature Extraction

Disease

Pre-processing

Segmentation Techniques[4] Segmentation Method Description Advantages Disadvantages

Histogram thresholding Histogram is constructed having peaks which correspond to a region.

Low computational complexity. No prior information needed.

Spatial details not considered, cannot guarantee the segmented regions to be contiguous.

Region based approaches Pixels are grouped in the homogeneous regions, and region merging, splitting or their combination is used.

Noise immune in edge detection approach.

High computational complexity. In region splitting segments appear square due to splitting scheme.

Edge detection approaches

Tries to locate the points having changes in gray level.

Works well for high contrast images.

Less immune to noise and doesn’t work well if the image have too many edges.

Segmentation Techniques[4] Segmentation Method Description Advantages Disadvantages

Fuzzy approaches Fuzzy operators, inference rules and properties are applied.

Approximate inference can be performed by fuzzy IF-THEN rules.

Computation can be intensive and determination of membership function is not an easy job.

Neural network approaches

Classification and clustering can be performed.

Less complicated and parallel nature of neural network can be used.

Training time is long and results can be affected by initialization.

Back

Different Colour Feature Extraction Techniques[1] • Dominant Colour Method

• Intensity Distribution

• Mean of Colour

• Nine Colour Characteristic Data

• HSI Colour Model

• Simple RGB

• Segmentation

• Direct Colour Mapping

Dominant Colour Method

• One of the techniques for the colour feature extraction used in the automated strawberry grading is the Dominant Colour Method.

• It represented the image of strawberry in L*a*b* colour model.

• Generally, the human sight is more interested in main colour of the image means that colour which appears frequently in the image.

• So this Dominant Colour Method was used on a* channel to extract the colour feature from the image.

Intensity Distribution Method

• Dates were graded according to their flabbiness.

• The best quality was given to the flabbiest date.

• In order to estimate the flabbiness they used the colour intensity distribution in the image.

• The image is then converted in to gray level and then colour intensity was found from that.

• Flabbiest date is brighter and less flabby date darker.

Mean of colour in images

• In order to determine the colour of the mango the mean of the colour array for red, green and blue was calculated as follows:

• Mean image = (Red value (Find size image) + Green value (Find size image) + Blue value (Find size image))/3

• Simple and easy to implement.

• Doesn't give the accurate colour.

Back

Classification[1]

• Neural Networks • A neural network consists of neurons, arranged in layers, which convert an

input vector into some output. • Each neuron takes an input, applies a function to it and then passes the

output on to the next layer.

• Fuzzy Logic • The simplest fuzzy rule-based classifier . • It is a fuzzy if-then system.

• Support Vector Machine

• SVM are supervised learning models . • Used for classification and regression analysis Back

Grading Quality Standards[6]

Grade designation

Grade Requirements Color Grade tolerances

Grade I Mangoes must be of superior quality They must be free of defects

5% Tolerance

Grade II

Mangoes must be of good quality. Mangoes may have slight defects in shape; slight skin defects due to rubbing or sunburn, and healed bruises not exceeding 2,3,4,5 sq. cm.

10% Tolerance

Grade III Mangoes may have slight defects in shape; slight skin defects due to rubbing or sunburn, and healed bruises not exceeding 4,5,6,7 sq. cm.

10% Tolerance

Back

Future Work

References 1) Image processing and machine learning for automated fruit grading system: A

Technical Review

2) Gonzalez, Rafael C., Richard E. Woods, and S. L. Eddins. "Image segmentation." Digital Image Processing (2002): 577-581

3) Website. [Online] http://webee.technion.ac.il/ido.pdf

4) Cheng, Heng-Da, X. H. Jiang, Ying Sun, and Jingli Wang. "Color image segmentation: advances and prospects." Pattern recognition 34, no. 12 (2001): 2259-2281.

5) A report: Post-harvest profile of mango ,year 2013

Any Q’s???