Systematic Review of a Convolutional Neural Network for Detecting Tomato Leaf Disease

Abstract

Abstract The agricultural sector is facing an increasing number of diseases of plants, particularly factors that have a significant impact on tomato plants, which can have a major effect on their quality and productivity. Timely management and action depend on accurate disease detection. Image classification tasks have made extensive use of Convolutional Neural Networks (CNNs). However, they face limitations in capturing global contextual information, which can lead to potential inaccuracies. This study reviews existing literature on the use of CNNs and hybrid models for tomato leaf disease detection, covering literature published between 2014 to 2024. A structured database search initially identified 2,591 records, of which 29 peer-reviewed studies met the inclusion criteria for detailed analysis. The study also examines the role of the nutrients present in tomato leaves, symptoms of disease, and their impact on productivity. The review evaluates CNN architectures, transfer learning models, lightweight networks, and hybrid approaches, focusing on datasets, preprocessing methods, and performance outcomes. Reported accuracies often exceed 95% on benchmark datasets, but performance declines sharply in field conditions due to variable environments, class imbalance, and limited dataset diversity. Three major challenges emerged: weak generalization beyond controlled data, high computational costs for deployment, and the absence of robust, field-oriented datasets. Recent advances, including transformer-enhanced CNNs, attention mechanisms, lightweight architectures, and pruning techniques, show promise in addressing these gaps. This review consolidates evidence, identifies limitations, and outlines future directions for plant disease detection that are resource-efficient, explainable, and real-time systems for sustainable agriculture.

Plant phenotyping relevance

トマト葉の病徴を画像から検出するCNN手法を体系的にレビューしており、植物病害状態の画像ベース表現型計測手法が中心です。

Code and data availability

This is a systematic review of CNN-based tomato leaf disease detection literature. It reports no original phenotyping measurements, image datasets, analysis code, or trained models of its own. Datasets like PlantVillage and Kaggle are discussed as resources used by the reviewed prior studies, not as paper-specific data

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