AI-Driven Crop Recommendation and Growth Prediction System Using Soil Fertility and Climate Intelligence

Agricultural decisions are increasingly affected by changing climate conditions, soil fertility, and the availability of timely technical guidance. This mini project presents an AI-Driven Agricultural Intelligence System that combines crop recommendation, soil fertility assessment, weather-risk prediction, yield estimation, fertilizer recommendation, and multilingual conversational support in one web platform. The system accepts soil and climate parameters such as nitrogen, phosphorus, potassium, pH, temperature, humidity, rainfall, location, crop, and season. The supplied implementation uses a Random Forest model for crop recommendation, XGBoost for weather-risk prediction, a Random Forest regressor for yield estimation, a Random Forest classifier for fertilizer recommendation, and a deterministic rule-based soilfertility module with model-independent fallback logic. A FastAPI backend performs validation, model loading, chained inference, weather integration, authentication, and response formatting, while a React-based Progressive Web Application provides the user interface. OpenWeatherMap supplies current weather context and Google Gemini AI supports multilingual agricultural explanations with offline FAQ and rulebased fallbacks. Reported module performance is approximately 88-95%, and representative API, fallback, authentication, translation, rate-limiting, and chatbot tests pass in the supplied project material. The project demonstrates how machine learning, web development, external APIs, and multilingual AI can be combined to deliver practical, accessible, and data-driven agricultural decision support. Keywords: Machine Learning, Agriculture, Crop Recommendation, Soil Fertility, Weather Risk, Yield Prediction, Fertilizer Recommendation, FastAPI, React, XGBoost, Google Gemini AI, Precision Agriculture.