Intelligent Cloud-Based System for Automated Crop Disease Diagnosis and Forecasting

Early and accurate diagnosis of crop diseases is critical for improving agricultural productivity and ensuring global food security. Traditional disease identification methods rely heavily on manual inspection and expert knowledge, which are time-consuming, subjective, and often inaccessible to small-scale farmers. This research proposes an intelligent cloud-based system for automated crop disease diagnosis and forecasting using advanced machine learning and deep learning techniques. The system integrates image-based disease detection with predictive analytics to provide real-time and future risk assessments. A convolutional neural network (CNN) model is employed to extract discriminative features from leaf images and classify various crop diseases with high accuracy. The trained model is deployed on a scalable cloud infrastructure, enabling remote access, centralized data management, and continuous model updates. In addition to diagnosis, time-series forecasting algorithms analyze historical disease occurrence patterns and environmental parameters such as temperature, humidity, and rainfall to predict potential outbreaks. The system architecture ensures efficient data processing, secure storage, and user-friendly interaction through web and mobile interfaces. Experimental evaluation demonstrates improved diagnostic accuracy and reliable forecasting performance compared to conventional methods. The proposed framework supports early intervention strategies, reduces crop losses, and promotes precision agriculture practices. By combining artificial intelligence with cloud computing, the system offers a scalable, cost-effective, and accessible solution for sustainable agricultural disease management.