Akademik Veri Yönetim Sistemi
Expert Systems with Applications, cilt.326, 2026 (SCI-Expanded, Scopus)
The identification of leaf water content in avocados can help to improve the irrigation regimes, thus alleviating the great water consumption of the avocado production. The classification of leaves according to their dehydration stage can be used as an early assessment tool for leaf water content identification. This study focuses on classifying avocado leaves based on their dehydration stage, using two approaches: hyperspectral reflectance values with classic machine learning models, and multi-spectral images with deep convolutional neural networks. The classic machine learning algorithms trained include random forest, k-nearest neighbours, support vector machine, bagging, decision tree, reduced error pruning tree, simple classification and regression tree, Bayesian networks, logistic model tree, linear discriminant analysis, multinomial logistic regression, and multilayer perceptron. The deep learning approach employed convolutinal neural network, specifically ConvNeXt-Tiny, MobileNetV3-Large, and ResNet18. The models were evaluated using common metrics such as accuracy, precision, f1 score, receiving operating characteristic curves, area under curve and confusion matrices. Among the machine learning models, linear discriminant analysis achieved the highest accuracy of 0.98. Meanwhile, ResNet18 outperformed the other CNNs, achieving an accuracy of 0.99 on the validation dataset. These results highlight the effectiveness of the proposed methods for classifying avocado leaves based on their dehydration level.