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

Chickpea is one of the most consumed legumes in the world. The classification of chickpea based on the size and morphological properties is important for the market. The objective of this study is to design and implement a computer vision system (CVS) integrated with artificial neural networks (ANN) for quality evaluation of chickpeas based on their size, colour, and surface morphology. The system is composed of a flat bed scanner for acquiring digital image and software that has been developed in Matlab for image analysis. Physical properties (length, width and volume) of the samples of chickpeas as well as their colour properties and surface characteristics have been determined by using the system, and results have been validated. High correlations have been found between the results from ANN‐integrated CVS and those obtained by callipers or professionally trained inspectors based on the experiments. Overall, percentages of correct classification have been determined as 95.4%, 87.6%, and 96.0% for colour, surface morphology, and shape evaluations, respectively.