A Computer Vision System Based on Majority-Voting Ensemble Neural Network for the Automatic Classification of Three Chickpea Varieties.

Razieh Pourdarbani,Juan Ignacio Arribas,Sajad Sabzi,Davood Kalantari,José Luis Hernández-Hernández

doi:10.3390/foods9020113

Abstract

Since different varieties of crops have specific applications, it is therefore important to properly identify each cultivar, in order to avoid fake varieties being sold as genuine, i.e., fraud. Despite that properly trained human experts might accurately identify and classify crop varieties, computer vision systems are needed since conditions such as fatigue, reproducibility, and so on, can influence the expert’s judgment and assessment. Chickpea (Cicer arietinum L.) is an important legume at the world-level and has several varieties. Three chickpea varieties with a rather similar visual appearance were studied here: Adel, Arman, and Azad chickpeas. The purpose of this paper is to present a computer vision system for the automatic classification of those chickpea varieties. First, segmentation was performed using an Hue Saturation Intensity (HSI) color space threshold. Next, color and textural (from the gray level co-occurrence matrix, GLCM) properties (features) were extracted from the chickpea sample images. Then, using the hybrid artificial neural network-cultural algorithm (ANN-CA), the sub-optimal combination of the five most effective properties (mean of the RGB color space components, mean of the HSI color space components, entropy of GLCM matrix at 90°, standard deviation of GLCM matrix at 0°, and mean third component in YCbCr color space) were selected as discriminant features. Finally, an ANN-PSO/ACO/HS majority voting (MV) ensemble methodology merging three different classifier outputs, namely the hybrid artificial neural network-particle swarm optimization (ANN-PSO), hybrid artificial neural network-ant colony optimization (ANN-ACO), and hybrid artificial neural network-harmonic search (ANN-HS), was used. Results showed that the ensemble ANN-PSO/ACO/HS-MV classifier approach reached an average classification accuracy of 99.10 ± 0.75% over the test set, after averaging 1000 random iterations.

Highlights

In 2017, global production of chickpea (Cicer arietinum L.) was estimated in almost 15 million tons/year
Discriminant properties selected by the artificial neural network-cultural algorithm (ANN-CA) include five features: average channels of first, second and third RGB color space, average first channel of Hue Saturation Intensity (HSI) color space, neighborhood entropy of 90◦ and 0◦ in Gray Level Co-ocurrence Matrix (GLCM), and the third channel of YCbCr color space
After examining different color spaces, HSI was selected as a suitable color space for segmentation purposes

Summary

Introduction

In 2017, global production of chickpea (Cicer arietinum L.) was estimated in almost 15 million tons/year. It is important to correctly identify the product variety This assessment is often made by human experts, but for various reasons, such as expert psychological changes, fatigue, and so on, errors in the evaluation of different varieties may occur. For this reason, it might be important to provide rapid detection systems using new technology. One can consider at least two main reasons why it might be important to provide rapid detection systems using new technology: first, different applications and price of each chickpea variety; second, separation of each chickpea variety when stored in the same tank, once they have been harvested. Some of these systems are based on physical and mechanical properties [3,4,5]

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