A Deep reinforcement Learning Approach for Early Detection of tomato plant disease using CNNs and machine learning Techniques

The present research attempts to improve tomato plant disease early detection strategies by using a variety of machine learning models, such as Recurrent Neural Networks (RNN), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN). Using the special advantages of these models, the major objective is to correctly diagnose diseases like Early blight, Bacterial leaf spot, Bacterial wilt, and Leaf curl. The work expands the foundation for disease detection by careful dataset construction, feature extraction, and preprocessing. The accuracy of CNN is the greatest at 97.5%, followed by SVM at 92.2%), KNN at 88.4%), and RNN at 86.2%. The results show remarkable accuracy. Our research demonstrates the promise of machine learning in precision agriculture by providing a useful tool for managing and diagnosing diseases early on, increasing crop output, and encouraging environmentally friendly farming methods.