<![CDATA[Accurate and timely detection of plant leaf diseases plays a vital role in ensuring crop health and supporting sustainable agricultural practices, particularly in the context of food security. Potato (Solanum tuberosum) is a high-potential food crop and a strategic commodity in many countries, including Indonesia, due to its nutritional value and adaptability to various agro-climatic conditions. However, its productivity is highly vulnerable to diseases such as early blight and late blight. This study presents a knowledge distillation framework for developing an efficient deep learning model to classify potato leaf diseases. EfficientNet-B5 was employed as the teacher model, achieving 100% accuracy, F1-score, Matthews Correlation Coefficient (MCC), and Cohen’s Kappa on the validation set. The student model, based on MobileNetV3-Small, successfully retained high predictive performance, achieving 99.07% accuracy, a macro F1-score of 0.9828, and a Cohen’s Kappa of 0.9833. MobileNetV3-Small significantly improved efficiency by reducing inference time by 67.58% (from 33.44 ms to 10.84 ms) and model size by 96.51% (from 111.54 MB to 3.89 MB) compared to EfficientNet-B5, making it highly suitable for real-time and resource-constrained applications. These results confirm that knowledge distillation enables the construction of lightweight models without significant loss of accuracy, making them suitable for mobile and edge-based agricultural applications.]]>
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