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

• URL: http://arxiv.org/abs/2109.02394v1
• Date: Mon, 6 Sep 2021 12:14:02 GMT
• Title: Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification
• Authors: Sabbir Ahmed, Md. Bakhtiar Hasan, Tasnim Ahmed, Redwan Karim Sony, and Md. Hasanul Kabir
• Abstract summary: This work proposes a lightweight transfer learning-based approach for detecting diseases from tomato leaves. It utilizes an effective preprocessing method to enhance the leaf images with illumination correction for improved classification. The proposed architecture achieves 99.30% accuracy with a model size of 9.60MB and 4.87M floating-point operations.
• Score: 0.36700088931938835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To ensure global food security and the overall profit of stakeholders, the importance of correctly detecting and classifying plant diseases is paramount. In this connection, the emergence of deep learning-based image classification has introduced a substantial number of solutions. However, the applicability of these solutions in low-end devices requires fast, accurate, and computationally inexpensive systems. This work proposes a lightweight transfer learning-based approach for detecting diseases from tomato leaves. It utilizes an effective preprocessing method to enhance the leaf images with illumination correction for improved classification. Our system extracts features using a combined model consisting of a pretrained MobileNetV2 architecture and a classifier network for effective prediction. Traditional augmentation approaches are replaced by runtime augmentation to avoid data leakage and address the class imbalance issue. Evaluation on tomato leaf images from the PlantVillage dataset shows that the proposed architecture achieves 99.30% accuracy with a model size of 9.60MB and 4.87M floating-point operations, making it a suitable choice for real-life applications in low-end devices. Our codes and models will be made available upon publication.

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