Prediction of Pistachio (Pistacia vera L.) Mass Based on Shape and Size Attributes by Using Machine Learning Algorithms

Size, mass, and shape attributes play a significant role in the quality assessment and post-harvest technologies of agricultural products. Pistachio is widely consumed worldwide, and Turkey has 3rd place in world pistachio production. In this study, physical attributes of 6 different pistachio cultivars (Beyaz Ben, Keten gömleği, Kirmizi, Siirt, Tekin, Uzun) were determined and machine learning algorithms (Multilayer Perceptron (MLP), k-Nearest Neighbor (kNN), Random Forest (RF), Gaussian processes (GP)) were used for mass prediction of these pistachio cultivars. Siirt and Tekin cultivars had the greatest gravitational and dimensional attributes. Among the pistachio cultivars, Kirmizi and Uzun had the greatest shape index and elongation values. Keten gömleği and Beyaz cultivars had the lowest averages of mass and area attributes both for nuts and kernels. Kernel and nut mass of pistachio had significant correlations with volume, geometric mean diameter, and projected and surface area ( p