AI-Based Crop Monitoring System: Cotton Disease Detection Using CNN

Agricultural production depends on the diseases that affect plants and pests; early recognition is essential to crop management. Traditional visual analysis is labor-intensive and subjective because it might depend on the expertise of an analyst; this paper presents an AI-driven cotton disease identification system using Convolutional Neural Networks (CNNs). This model is supported by a web dashboard and a mobile app. The model was trained using 9,495 images of cotton leaves from nine categories: Alternaria Leaf Spot, Aphids, Army Worm, Bacterial Blight, Fusarium Wilt, Healthy, Powdery Mildew, Target Spot, and Verticillium Wilt. All images were, in other words, scaled to 224 × 224 pixels and normalized to zero-to-one in the real-life scenario. Augmentation such as rotation, shifting, zooming, horizontal flipping, and shearing is implemented during the training process. What I mean to say is that the dataset was split into 7,594 training images, 947 validation images, and 954 test images. Custom CNN reached the accuracy of 95.18%, with the macro F1-score of 93.49% and the weighted F1-score of 95.15%.Flask-based backend supports the application, while the web dashboard provides the information and the model's analytics. Further evaluation using images collected under the mixed real-field conditions is needed to establish concrete reliability and generalization.