Crop Yield Prediction

Crop yield prediction is a critical aspect of modern agriculture, as it directly impacts food security,  economic planning, and resource management. Traditional methods of yield estimation often rely  on historical data and manual analysis, which are limited in handling complex relationships  between environmental and agricultural factors. Recent research has demonstrated that machine  learning techniques provide more accurate and efficient solutions for crop yield prediction.  This study presents a machine learning-based approach for predicting crop yield using multiple  parameters such as rainfall, temperature, humidity, soil properties, and crop type. Various  algorithms, including Decision Tree, Random Forest, and Gradient Boosting, are analyzed based  on insights from existing literature. Among these, ensemble methods like Random Forest and  hybrid models have shown superior performance due to their ability to handle large datasets and  nonlinear patterns.  The methodology involves data collection from agricultural and meteorological sources, followed  by preprocessing techniques such as data cleaning, normalization, and feature selection. The  models are trained and evaluated using performance metrics like Mean Squared Error (MSE) and  R² score to ensure accuracy and reliability.