AI-Driven Cotton Leaf Disease Detection and Management System

As important as agriculture is in the maintenance of food regimes around the globe, crop diseases remain a major challenge to agricultural productivity, especially for farmers who do not have access to expert diagnosis in time. Sometimes, manual detection techniques are slow, unreliable, and require the presence of specialists, which may slow down the implementation of corrective measures and cause the loss of considerable yields. In order to overcome the mentioned obstacle, this paper introduces an AI-based Cotton Leaf Disease Detection and Management System that implements the VGG16 model to deliver fast, accurate, and accessible diagnostic services. The system applies the VGG16 convolutional neural network to cotton leaf images with great accuracy, supported by preprocessing and data augmentation strategies that promote the performance of the system under changing lighting and environmental conditions. Furthermore, a conversational chatbot interface allows users to upload leaf images, obtain immediate predictions, and receive clear and actionable disease management recommendations. The platform is developed with React and can be accessed on any device with simplicity and ease. The results of the experiment indicate a high level of prediction accuracy and positive user interaction, which means that the system can support the process of early detection, promote smart farming, and become a part of sustainable crop management.