SMART CROP RECOMMENDATION SYSTEM

Abstract: Choosing the right crop for a field strongly affects yield, resource use and the economic stability of rural households. Conventional expert advice and generalised advisories seldom reach field-level precision, and many digital tools either return a prediction with no explanation or mix model outputs with plain statistical records. This paper presents Smart Crop Recommendation, a web application that keeps the two apart: a machine learning recommendation engine and a regional statistical lookup. The recommendation engine is a Random Forest classifier of 100 decision trees, trained on a 2,200-sample dataset covering 22 crops. It takes seven soil and weather inputs: Nitrogen, Phosphorus, Potassium, pH, temperature, humidity and rainfall. Rather than a single unexplained answer, it ranks every crop and returns the top three with their class probabilities and a comparison of the entered values against recorded values. The lookup component gives a district-level profile for 38 districts of Tamil Nadu, taken from the official statistical handbook (2021–22), and involves no machine learning. On a held-out test set of 440 samples (20% of the data), the model reached an accuracy of 99.32% and a top-3 hit rate of 100.00%. The system also validates user inputs and supports United Nations Sustainable Development Goal 2 (Zero Hunger).

Keywords: Precision Agriculture, Crop Recommendation, Random Forest, Explainable AI, Machine Learning, Agro- Climatic Profile, Sustainable Development Goal 2.