Comparative evaluation of quality properties of chickpea seed (Cicer arietinum L.) grown under different irrigation conditions by using machine learning

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Chickpea is an important edible legume consumed worldwide because of rich nutrient composition. The physical parameters of chickpea are crucial attributes for design of processing and classification systems. In this study, effects of seven different irrigation treatments on size, shape, mass, and color properties of chickpea seeds were investigated, and machine learning algorithms were used to estimate mass and color attributes of chickpea seeds. The results showed that Multilayer Perceptron (MLP) had the greatest correlation coefficients for mass (0.9997) and chroma (0.9997). The MLP yielded better outcomes than Random Forest for both mass and color estimation. In terms of physical attributes, the best results were obtained in I1 (rainfed) and I5 (irrigation at 50% flowering and 50% pod fill) irrigation treatments. Additionally, single or couple irrigations at different physiological stages instead of full irrigation treatment might be sufficient to improve the physical attributes of chickpea.