Quality evaluation of chickpeas using an artificial neural network integrated computer vision system

We synthetically applied computer vision, genetic algorithm and artificial neural network technology to automatically identify the vegetables (tomatoes) that had physiological diseases. Initially tomatoes’ images were captured through a computer vision system. Then to identify cavernous tomatoes, we analyzed the roundness and detected deformed tomatoes by applying the variation of vegetable’s diameter. Later, we used a Genetic Algorithm (GA) based artificial neural network (ANN). Experiments show that the above methods can accurately identify vegetables’ shapes and meet requests of classification; the accuracy rate for the identification for vegetables with physiological diseases was up to 100%. [Nature and Science. 2005; 3(2):52-58].

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Design and fabrication of an intelligent control system for determination of watering time for turfgrass plant using computer vision system and artificial neural network

Precision Agriculture ◽

10.1007/s11119-018-9618-x ◽

2018 ◽

Vol 20 (5) ◽

pp. 857-879 ◽

Cited By ~ 1

Author(s):

Maryam Nadafzadeh ◽

Saman Abdanan Mehdizadeh

Keyword(s):

Neural Network ◽

Computer Vision ◽

Artificial Neural Network ◽

Control System ◽

Intelligent Control ◽

Vision System ◽

Computer Vision System ◽

Intelligent Control System ◽

Artificial Neural

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

Foods ◽

10.3390/foods9020113 ◽

2020 ◽

Vol 9 (2) ◽

pp. 113 ◽

Cited By ~ 7

Author(s):

Razieh Pourdarbani ◽

Sajad Sabzi ◽

Davood Kalantari ◽

José Luis Hernández-Hernández ◽

Juan Ignacio Arribas

Keyword(s):

Neural Network ◽

Computer Vision ◽

Artificial Neural Network ◽

Vision System ◽

Color Space ◽

Majority Voting ◽

Hsi Color Space ◽

Artificial Neural ◽

Hybrid Artificial Neural Network

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.

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