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

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