The increasing prevalence of plant diseases, limited water resources, and fragmented agricultural knowledge among smallholder farmers pose serious threats to sustainable crop production. In response to these challenges, this research introduces an AI-Powered Farmer Support System—an integrated platform engineered to deliver real-time, intelligent farming assistance without relying on complex IoT infrastructure. The system is architected around four key modules: a convolutional neural network (InceptionV3) for early-stage plant disease identification, enhanced by a generative AI engine to provide region-specific treatment recommendations; a crop recommendation engine that synthesizes localized weather forecasts and soil attributes through dynamic API integration; a data-driven irrigation advisory tool leveraging predictive analytics to optimize water distribution; and an interactive consultation module powered by natural language processing to facilitate expert-level guidance. The platform has been developed using a hybrid architecture combining the MERN stack and Flask-based microservices, allowing it to operate in both sensor-rich and sensor-deprived environments. Field testing across varied agricultural regions indicated a plant disease classification accuracy of 91%, with the crop advisory module achieving a 94% precision rate in suggesting optimal crop types. Moreover, irrigation efficiency improved by up to 35% due to predictive scheduling, while expert validation rated AI-generated recommendations as over 85% relevant. This research highlights the system’s ability to democratize access to precision farming technologies, particularly in regions lacking technical infrastructure. The proposed solution exemplifies a scalable, inclusive, and sustainable model for next-generation digital agriculture, aligning with global food security and climate resilience objectives.

Keywords: Precision Farming, Crop Recommendation, Plant Disease Detection, Smart Irrigation,Real-Time Agricultural Advisory, Farm Decision Support System