Using soft computing and leaf dimensions to determine sex in immature Pistacia vera genotypes. (April 2021) - Rezaei, Mehdi; Rohani, Abbas; Heidari, Parviz; Lawson, Shaneka
Graphical abstract: Highlight: The soft computing methods (KNN, SVM, SOM, ANFIS and RBF) were used to sex determination of the pistachio trees. RBF neural network is the best classifier for discriminating male and female pistachio genotypes. The classification accuracy of RBF neural network based on geometric features of mature leaves was 84.44%. Abstract: Pistachio ( Pistacia vera ) is a dioecious tree species in the cashew family originating from Central Asia and the Middle East. The sex of pistachio genotypes cannot be established until inflorescence growth during the reproductive stage. The ability to determine sex before flowering would be extremely beneficial for pistachio growers and breeders. In this study, different methods of soft computing, using classifiers (i.e. KNN, SVM, SOM, ANFIS and RBF) were employed to determine pistachio sex based on geometric features of mature leaves. Data were collected from more than 900 leaves representing 45 pistachio genotypes before sexual maturation. Separate training data set sizes (20, 40, 60 and 80%) were used to evaluate the generalizability of each classifier. Results indicated the KNN classifier was the most accurate (>95%) for sex determination during training phases however, accuracy during testing phases varied from 52 to 60% for the four training data set sizes. Training phase accuracy with the 80% data set ranged from 83 to 64% with the respective arrangement of RBF, ANFIS, SVM, and the SOM classifiers. In the test phase, classifier accuracy varied from 88.33% (RBF) to 60% (KNN). Our results indicated RBF could reliably be used to differentiate between male and female pistachio genotypes. Thus, soft computing models are useful tools for predicting sex in pistachio based on leaf dimensions.