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To ensure global food security and the overall profit of stakeholders, theimportance of correctly detecting and classifying plant diseases is paramount.In this connection, the emergence of deep learning-based image classificationhas introduced a substantial number of solutions. However, the applicability ofthese solutions in low-end devices requires fast, accurate, and computationallyinexpensive systems. This work proposes a lightweight transfer learning-basedapproach for detecting diseases from tomato leaves. It utilizes an effectivepreprocessing method to enhance the leaf images with illumination correctionfor improved classification. Our system extracts features using a combinedmodel consisting of a pretrained MobileNetV2 architecture and a classifiernetwork for effective prediction. Traditional augmentation approaches arereplaced by runtime augmentation to avoid data leakage and address the classimbalance issue. Evaluation on tomato leaf images from the PlantVillage datasetshows that the proposed architecture achieves 99.30% accuracy with a model sizeof 9.60MB and 4.87M floating-point operations, making it a suitable choice forreal-life applications in low-end devices. Our codes and models are availableat https://github.com/redwankarimsony/project-tomato.

Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification