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 gomlegi, 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 gomlegi 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 < 0.01). Present findings revealed that Gaussian processes had the greatest correlation coefficients (0.976 for nut mass and 0.948 for kernel mass prediction) and the lowest RMSE values (0.038 for nut and 0.029 for kernel mass prediction). This algorithm was respectively followed by Multilayer Perceptron and Random Forest algorithms. Present findings revealed that Gaussian processes, Multilayer Perceptron, and Random Forest algorithms could potentially be used for mass prediction of pistachio cultivars.

Automated summary

The size, mass, and shape attributes play a significant role in the quality assessment and post-harvest technologies of agricultural products. Various pistachio cultivars were analyzed for their physical attributes, with Gaussian processes showing the highest correlation coefficients and lowest RMSE values for mass prediction among the machine learning algorithms used in the study. This suggests the potential of using Gaussian processes for mass prediction of pistachio cultivars.