Convolutional Neural Network-Based Detection of Tomato Leaf Diseases Using an Augmented Image Corpus

In addition to disease identification, recent advancements aim to provide actionable advice to farmers by recommending fertilizers or supplements specific to the identified condition. This review not only surveys CNN models for tomato disease classification but also presents an outlook on their integration into intelligent decision-support systems. We also examine the challenges faced in real-world implementations and propose practical solutions to overcome these limitations for widespread deployment. Tomato is one of the most cultivated and consumed vegetables globally, but it is highly susceptible to a variety of plant diseases, which significantly impact yield and quality. Traditional methods of disease detection are labor-intensive and prone to human error. With the advancement in artificial intelligence, particularly deep learning, Convolutional Neural Networks (CNNs) have shown great promise in automated plant disease detection using image data. This review explores the methodologies of using CNNs for tomato plant disease classification via leaf images and discusses how such systems can be extended to recommend suitable supplements or treatments. It critically evaluates existing models, identifies gaps in current research, and proposes future directions for building scalable, real-time systems for farmers and agricultures. Keywords Tomato Plant Disease Detection, Machine Learning, Deep Learning, Image Processing, PlantVillage Dataset, IoT, Agricultural Technology , Convolutional Neural Networks , Support Vector Machines ,K-Nearest Neighbours , computational complexity , Smart farming, Disease classification, Supplements, Multimodal approaches .