• Mrs.Vanishri Sataraddi
• Sanjana Kumari
• Shreeya P
• Shwetha D

The goal of this research is to develop an automated system that properly classifies nine different tomato leaf diseases using convolutional neural networks (CNNs). The system tackles many diseases, including Bacterial Spot, Late Blight, and Tomato Mosaic Virus, by using a large dataset that was acquired from Kaggle. The main objective is to meet the pressing need for quick and accurate diagnosis in tomato crops by facilitating early disease detection through the application of deep learning techniques. The current process entails training a CNN model and incorporating it into a user-friendly graphical interface with the goal of giving farmers and agriculture enthusiasts a useful and accessible tool for quickly identifying illnesses.

Convolutional Neural Networks (CNNs), ResNet, DenseNet

"Deep Learning Approaches for Tomato Leaf Disease Detection Implementation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.446-458, May-2024, Available :http://www.jetir.org/papers/JETIRGG06070.pdf

"Deep Learning Approaches for Tomato Leaf Disease Detection Implementation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. pp446-458, May-2024, Available at : http://www.jetir.org/papers/JETIRGG06070.pdf