Agriculture is an area where a small difference in information can affect a farmer’s decision about what to grow, how to manage a crop and whether an expected return is realistic. Farmers may have useful experience, but information about soil conditions, weather, crop diseases, expected yield and market prices is not always available in one place. Contract farming creates an additional information problem because farmers and contractors may not always have the same information when discussing prices and terms. SmartCrop AI is proposed as a practical digital platform that brings these requirements together. The system combines machine-learning based crop and yield prediction, soil-suitability assessment, an AI advisory component, simulated IoT data, mandi-price information and farmer-oriented reporting. Random Forest and Gradient Boosting are used in the prototype for prediction and suitability scoring, while an LLM-based Kisan Mitra component is intended to explain results in natural language. The design is based on findings from five related studies covering contract farming, IoT-based crop recommendation, AI advisory, sustainability assessment and yield-demand based crop recommendation. The present work focuses on the system design and prototype architecture; real-field sensor validation and large-scale farmer testing are considered future work.

Keywords: Precision Agriculture, Crop Recommendation, Machine Learning, Artificial Intelligence, IoT, Random Forest, Yield Prediction, Contract Farming, Mandi Market Intelligence, AI Advisor.