Cotton Disease Detection Using Deep Transfer Learning with RESNET152V2 and Integrated LLM-Based Diagnostic Assistant

Cotton contributes significantly to agricultural income, yet diseases continue to affect productivity and farmer profit. Manual inspection of crops is still common practice, however it demands time, attention and becomes difficult when dealing with large agricultural fields. With this motivation, a computer-based solution for cotton plant disease identification was developed using deep learning. The dataset used for experimentation was sourced from Kaggle and consisted of 1957 training samples, 324 validation samples and 18 images for testing. Three transfer learning architectures were tested in the study namely ResNet50, InceptionV3 and ResNet152V2. Among these, ResNet152V2 produced the most promising results, achieving an accuracy of 98.10 percent with a loss of 0.2066, and the validation accuracy reached 100 percent within 20 epochs. The trained model is capable of classifying four categories that include diseased cotton leaf, diseased cotton plant, fresh cotton leaf and fresh cotton plant. A frontend interface was built using React so that users such as farmers can upload images captured from a mobile camera or drone. The model processes the input and returns the prediction on the spot. To improve usability, an LLM based module is integrated to provide simple explanations and preventive suggestions for the detected disease. The overall study shows that the system can assist in early diagnosis of cotton diseases and help reduce crop loss by enabling timely intervention.