A Detailed Survey of Deep Learning Frameworks for Early Prediction and Classification of Tomato Leaf Diseases
DOI:
https://doi.org/10.5281/Keywords:
Tomato Leaf Disease, Precision Agriculture, Image Classification, Explainable AI (XAI), Deep LearningAbstract
Tomato leaf diseases caused by bacterial, viral, and fungal pathogens constitute a significant global barrier to agricultural production and tomato crop quality. Early and correct diagnosis/detection are crucial to limit economic losses and sustainable farming. Using leaf images for identifying diseases of the plant has proved to be a good solution, and recently with the advent of Artificial Intelligence (AI) and Deep Learning (DL) it has been possible to find a more efficient solution. This paper offers a thorough evaluation of DL frameworks for the early detection and classification of tomato leaf disease. examination of several methods, including hybrid DL models, Vision Transformers (ViTs), Convolutional Neural Networks (CNNs), transfer learning models, and their accuracy and performance. The study also explores and discusses commonly used agricultural datasets, preprocessing strategies, feature extraction techniques, and performance assessment measures (such as accuracy (ACC), precision (PRE), recall (REC), F1-score (F1), and ROC-AUC). Furthermore, recent advancements in Explainable AI (XAI), Edge AI and small DL models for Smart Agriculture applications were highlighted. Lastly, challenges, limitations and future directions of real-time tomato disease detection systems are discussed.
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Copyright (c) 2026 Pankaj Modi, Dr. Nilesh Jain (Author)
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