Akademik Veri Yönetim Sistemi
FOOD ANALYTICAL METHODS, cilt.15, ss.2260-2273, 2022 (SCI-Expanded, Scopus)
The most important principal quality attributes of seeds are shape, size, and mass. These parameters play a critical role in
the design of classifer and grading machines. This study was conducted to develop classifcation models for distinguishing the soybean seeds based on shape, size, and mass attributes. The seeds of soybean varieties of Bravo, Ceyhan, Çevik,
İlksoy, and Traksoy were classifed in pairs. Four diferent machine learning algorithms (random forest, RF; support vector
machine, SVM; Naïve Bayes, NB; and multilayer perceptron, MLP) were used to evaluate the classifcation performance.
In all cases, the soybean seeds of Ceyhan and Traksoy varieties were classifed with the greatest accuracy as 90.00% for the
RF classifer and 89.00% for MLP. The variety pairs that followed these varieties with the highest accuracy were Çevik and
İlksoy (88.00%, MLP) and Çevik and Traksoy (87.50%, RF). The highest mass (0.19 g), volume (155.02 mm3
), geometric
mean diameter (6.65 mm), and projected area (34.80 mm2
) values were obtained from Traksoy variety. The Pillai trace and
Wilks’ lambda results revealed that diferences in physical attributes of the soybean varieties were signifcant (p<0.01). In
Wilks’ lambda statistics, the unexplained part of the diferences between the groups was found to be 23.0%. Traksoy and
Çevik varieties with the highest Mahalanobis distances had similar attributes. Present fndings showed that MLP and RF
could potentially be used for the classifcation of soybean varieties.