Lightweight Early and Late Blight Detection on Potato Leaves via Knowledge Distillation for Precision Agriculture

Ahmad Faisol; Deddy Rudhistiar; Betty Dewi Puspasari; Thesa Adi Saputra Yusri

doi:10.52436/1.jutif.2026.7.4.5181

Authors

Ahmad Faisol Information Engineering, Institut Teknologi Nasional Malang, Indonesia
Deddy Rudhistiar Information Engineering, Institut Teknologi Nasional Malang, Indonesia
Betty Dewi Puspasari Computer Science and Information Engineering, National Dong Hwa University, Taiwan
Thesa Adi Saputra Yusri Mathematics, Universitas Negeri Yogyakarta, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.4.5181

Keywords:

EfficientNetB5, Knowledge Distillation, MobileNetV3-Small, Potato Leaf Disease Classification

Abstract

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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