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

SAĞLAM C., ÇETİN N.

FOOD ANALYTICAL METHODS, cilt.15, sa.3, ss.739-750, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Sayı: 3
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1007/s12161-021-02154-6
  • Dergi Adı: FOOD ANALYTICAL METHODS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Agricultural & Environmental Science Database, CAB Abstracts, Compendex, Food Science & Technology Abstracts, INSPEC, Veterinary Science Database
  • Sayfa Sayıları: ss.739-750
  • Anahtar Kelimeler: Pistachio, Mass, Multilayer Perceptron, Gaussian processes, Random Forest, ARTIFICIAL NEURAL-NETWORKS, PHYSICAL-PROPERTIES, AERODYNAMIC PROPERTIES, RANDOM FOREST, KERNEL, CULTIVARS, FRUITS, NUT, DISCRIMINATION, CLASSIFICATION
  • Ankara Üniversitesi Adresli: Hayır

Özet

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.