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

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.