Agriculture forms the backbone of the Indian economy, yet smallholder farmers continue to face critical challenges including crop failure, rampant plant disease, unpredictable weather, and limited access to expert advisory services. This paper presents Smart-Kheti, a web-based AI-powered smart agriculture platform designed to democratize data-driven decision support for farmers. The proposed system integrates a personalized crop recommendation engine utilizing soil nutrient parameters (N, P, K), pH, temperature, humidity, and rainfall processed through an XGBoost-based multi-class classifier; an automated plant disease detection module employing a Convolutional Neural Network (CNN) trained on the PlantVillage dataset and deployed via TensorFlow Lite for server-side inference and TensorFlow.js for offline client-side inference; and a yield prediction module utilizing XGBoost regression on multi-year historical agricultural data. The platform employs a full- stack architecture with React.js and TypeScript on the frontend and Python FastAPI on the backend, containerized using Docker for scalable deployment. Additional features include a profit calculator, real-time market insights from government data APIs, offline support, and multilingual accessibility. Experimental evaluation demonstrates crop recommendation accuracy of 97.4%, disease detection accuracy of 93.7%, and yield prediction RZ of 0.87.
singh et al. (Thu,) studied this question.