Detection of Tomato Leaf Diseases for Agro-Based Industries Using Deepnet Network

The detection and classification of tomato leaf diseases play a critical role in ensuring healthy crop production and minimizing agricultural losses. This research focuses on the development of an efficient and accurate system for disease identification using a DeepNet network architecture. The proposed approach leverages advanced deep learning techniques to identify common diseases such as early blight, late blight, and leaf mold from tomato leaf images. A curated dataset comprising healthy and diseased leaf samples was preprocessed and augmented to enhance model robustness. The DeepNet model demonstrated superior performance compared to traditional convolutional neural networks (CNNs), achieving an accuracy of XX% in disease detection. Evaluation metrics such as precision, recall, and F1-score further validated the model's effectiveness. The system offers real-time disease detection capabilities, making it highly suitable for deployment in agro-based industries to support precision farming. This research provides a scalable and cost-effective solution to improve agricultural productivity and reduce dependency on manual inspection methods.