In recent years, the convergence of Artificial Intelligence and the Internet of Things (AIoT) has emerged as a transformative force in precision agriculture. This research presents SmartCrop, a next-generation crop intelligence system that seamlessly blends real-time soil sensing with generative AI to offer tailored agricultural insights. The system utilizes affordable IoT sensors to capture critical soil parameters such as pH, moisture, temperature, and nutrient levels, while integrating live, location-specific weather data through public APIs. For regions lacking sensor infrastructure, a custom-developed Soil API—built from geo-tagged historical sensor data—ensures uninterrupted service and wider accessibility. At the core of SmartCrop lies the integration of OpenAI’s generative models, which provide personalized crop recommendations, rotation strategies, and sustainability insights. Engineered using the MERN stack (MongoDB, Express.js, React.js, Node.js), the platform delivers a responsive, scalable, and user-friendly experience for farmers across varying digital literacy levels. Field deployments across multiple Indian districts reveal notable gains in crop yield, resource efficiency, and adoption rates when compared to traditional methods. The integration of OpenAI with real-time environmental data establishes a robust framework for precision agriculture. The proposed system not only addresses scalability and accessibility challenges but also demonstrates significant improvements in yield optimization and resource management. By leveraging cutting-edge technologies, this research lays the groundwork for sustainable and efficient farming practices globally.

Keywords: Precision Agriculture, AIoT (Artificial Intelligence of Things), Generative AI, Real-Time Soil Sensing, Crop Recommendation System, Sustainable Farming