Exploring Advanced Methods for Tomato Leaf Disease Detection: A Convolutional Neural

CNN(Convolutional Neural Network) establishes an ongoing, Cutting-edge method in image processing with significant potential and encouraging outcomes. Having demonstrated its effectiveness across diverse applications, Convolutional Neural Network has now made significant inroads into the agricultural domain as well. We conducted a survey of 38 research studies that employed Convolutional Neural Network techniques to address a range of research challenges related to tomato plants.We explored the realms of tomato plant research where Convolutional Neural Networks were implemented, delving into the employed data preprocessing techniques, as well as the utilization of transfer learning and augmentation techniques.We analyzed dataset details such as the sources utilized, the total number of images, the number of classes, and the train-test-validation ratio employed. Furthermore, we conducted comparisons across different Convolutional Neural Network architectures and discussed the resulting outcomes.The findings indicated that Convolutional Neural Networks consistently outperformed other image processing techniques. However, it was noted that the performance of Convolutional Neural Networks is heavily influenced by the quality and characteristics of the dataset used.