A Deep Learning Approach to Tomato Disease Classification Using a CNN-LSTM Hybrid Network

Abstract

Agriculture is a crucial sector that meets the fundamental nutritional needs of humanity. Plant diseases exacerbate economic and food security issues for nations and hinder their agricultural planning. Conventional techniques for identifying plant diseases necessitate considerable labour and time. As a result, numerous scholars and institutes endeavour to tackle these challenges through sophisticated technical approaches. Deep learning-based plant disease detection shows significant progress and optimism over traditional techniques. When trained using extensive, high-quality datasets, these systems detect diseases on plant leaves in their early stages. This article conducts a systematic evaluation of deep learning methodologies in plant disease detection by analyzing a number of research publications between 2015 and 2025. Our research examines three specific areas: the categorization, detection, and segmentation of diseases on plant leaves, while rigorously evaluating publicly available datasets. This systematic review offers an in-depth evaluation of the existing literature, describing deep learning architectures, the most frequently studied tomato diseases, datasets, challenges encountered, and diverse perspectives. It offers new perspectives for researchers in the field of agriculture. In addition, it addresses the main challenges of identifying agricultural diseases.: datasets, barriers, and diverse perspectives. It offers new perspectives for researchers in the agricultural field. In addition, it addresses the main problems related to agricultural diseases.