COTTON PLANT DISEASE PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS (CNN) AND FLASK

– This document presents a deep learning-based approach for the early detection of cotton plant diseases using Convolutional Neural Networks (CNN). Cotton is a crucial cash crop, but its yield is significantly affected by various plant diseases, leading to economic losses for farmers. Traditional disease detection methods rely on manual inspection, which is time-consuming, error-prone, and requires expert knowledge. To address this challenge, we propose an automated system that leverages CNNs for image classification and Flask for web deployment. The CNN model is trained on a dataset of cotton leaf images and classifies diseases such as Bacterial Blight, Powdery Mildew, and Target Spot. The model is designed with multiple convolutional layers that extract intricate features from images, ensuring high classification accuracy. Image preprocessing techniques such as normalization and augmentation are applied to enhance model performance. The trained model is then integrated into a web-based platform using Flask, allowing users to upload images for real-time disease prediction. The application is designed to be user-friendly, making it accessible to farmers and agricultural researchers. Experimental results demonstrate that the CNN model achieves high accuracy compared to traditional machine learning methods. This system enhances early disease detection, reduces dependency on experts, and improves crop yield by providing timely diagnosis and intervention measures. Additionally, the scalability of the proposed model allows for further expansion to detect more plant diseases and integrate real-time field monitoring. The adoption of AI-driven disease detection in agriculture can significantly contribute to sustainable farming and improved food security.