Tomato is a highly valuable crop in the Indian market, cultivated in large quantities. However, tomato plants are susceptible to various diseases, which can lead to significant reductions or complete destruction of the crop, resulting in substantial economic losses for farmers. Therefore, the early and accurate detection of these diseases is crucial to minimize such losses. In recent years, numerous methods have been proposed to detect plant diseases, leveraging advancements in technology. Despite these efforts, achieving high accuracy in detecting tomato plant diseases remains a challenge. In this paper, we present a comprehensive study where we explore the effectiveness of deep learning models in detecting and classifying diseases in tomato plants