Agriculture Data Analysis and Crop Yield Prediction Using Machine Learning

Abstract

Agriculture Data Analysis and Crop Yield Prediction is a project aimed at improving the accuracy and reliability of crop yield forecasting through advanced data-driven techniques. The work utilizes a rich dataset comprising features such as crop type, cultivation state, season, crop year, annual rainfall, fertilizer and pesticide usage, and area under cultivation. Multiple machine learning algorithm including Linear Regression, Random Forest, Gradient Boosting, XGBoost, and other are trained and evaluated to identify the most effective model for predicting yields. The project features a Flask-based web application with an intuitive interface that supports several key functionalities: predicting the yield for a selected crop, determining the best crop for given environmental and input conditions, displaying yield comparisons across all crops, and suggesting optimal land partitioning for maximizing productivity. By abstracting away the need for manual analysis and enabling dynamic, data-driven decisions, the project addresses pressing agricultural challenges such as climate variability, input optimization, and sustainable food production.