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

TL;DR

This paper introduces a lightweight, transfer learning-based deep neural network for accurate tomato leaf disease classification, optimized for low-end devices with high accuracy and low computational requirements.

Contribution

It proposes a novel, efficient model combining MobileNetV2 with runtime augmentation, suitable for real-world deployment on resource-constrained devices.

Findings

Achieves 99.30% accuracy on PlantVillage dataset

Model size is only 9.60MB, with 4.87M FLOPs

Effective illumination correction improves classification performance

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 are available at https://github.com/redwankarimsony/project-tomato.