Betel Leaf Disease Classification Under Limited Data Using ConvNeXt-Tiny and UCB-Guided Adaptive Augmentation

Automated betel leaf disease recognition remains challenging under limited and imbalanced data conditions. This study investigates an Upper Confidence Bound (UCB)-guided adaptive augmentation framework for the classification of healthy leaves, leaf rot, and leaf spot. To reduce the risk of performance inflation caused by augmented-data leakage, only the 2037 original images of a publicly available betel leaf disease dataset were used for stratified training, validation, and test partitioning, while augmentation was restricted to the training stage. Several representative deep learning architectures, including ResNet, DenseNet, EfficientNet, MobileNet, and ConvNeXt-Tiny, were evaluated under a unified experimental setting. In the proposed approach, augmentation-policy selection is formulated as a multi-armed bandit problem, where the UCB controller dynamically selects among predefined augmentation arms comprising different RandAugment magnitudes and Mixup settings based on validation performance. Among the evaluated configurations, ConvNeXt-Tiny integrated with UCB-guided augmentation achieved strong overall performance, with an accuracy of 98.04%, a macro-F1 score of 97.87%, and a micro-average AUC of 0.999. These findings indicate that combining ConvNeXt-Tiny with adaptive augmentation can provide an effective approach for betel leaf disease classification under the investigated limited-data setting, with potential for future deployment in resource-constrained agricultural monitoring applications.