Mitigating Imbalanced Citrus Disease Image Datasets with Oversampling - Kaslani; Arya Gunawan; Nana Suarna; Agus Bahtiar; Indra Wiguna Marthanu (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2026)
Dataset imbalance is a critical challenge in plant disease image classification because it causes bias towards the majority class. This study evaluates the effectiveness of augmentation-based oversampling techniques on the classification performance of citrus leaf images using the MobileNetV2 architecture. The four leaf disease classes classified include Greening, Fresh, Canker, and Blackspot. The dataset was obtained from a public repository and processed through preprocessing (resize, normalization) and augmentation (rotation, flipping, zoom) stages. The model was trained and tested in two scenarios: baseline (unbalanced data) and mitigation (data balanced through augmentation). The experimental results show that the mitigation approach was able to increase accuracy from 91.92% to 93.94%. The F1-score, precision, and recall values also increased significantly, especially in the minority class. Evaluation using a confusion matrix reinforced the finding that augmentation-based oversampling is effective in reducing classification errors. This study shows that the integration of augmentation techniques and MobileNetV2-based transfer learning can significantly improve classification performance and contribute to the development of early detection systems for plant diseases in precision agriculture.