Smart Agriculture: Combining Crop Recommendation, Yield Prediction, and Environmental Analyzers for Optimized Farming

Climate changes, resource limitations and demands for sustainable practices has led to significant challenges to modern agriculture. Therefore in order to tackle this, our research addresses these challenges and suggests a system that integrates machine learning models and IoT technologies to predict crop yields and recommend suitable crops based on environmental conditions. Existing approaches lack real time capabilities thus fail to address farmer's decision making needs. To eliminate these challenges, we propose a web-based application that includes high level machine learning models for crop yield recommendation and prediction. The web app features a integrated interface for users to input data or link to IoT-based sensors for real-time evaluation of factors such as humidity, rainfall, soil health, and temperature. The application's constant observation and adjustable functions confirms that insights remain suitable under altering environmental factors. The system analyzes various machine learning models, involving Random Forest, Gradient Boosting, and Support Vector Machines, to determine the most precise and optimized algorithms for prediction tasks. Our models illustrates high accuracy of 97% for crop recommendation and 0.98 value for R2 yield prediction. Incorporating these components, the recommended system enhances crop management decisions, makes resource use more effective, and improves productivity. The research asserts with a discussion of its participation to sustainable farming and highlights future advancements, such as integrating further data sources and high level machine learning techniques.