Predicting Agricultural Output Using Machine Learning
The Crop Yield Regression Model is a machine learning-based solution designed to accurately predict crop yields based on various agricultural, environmental, and climatic factors. By leveraging historical data and statistical modeling techniques, this model helps farmers, agronomists, and policymakers make informed decisions regarding crop planning, resource allocation, and food security strategies.
This regression model takes into account variables such as soil quality, rainfall, temperature, fertilizer usage, and past yield records to estimate the expected production for a given crop. By training on real-world data, the model identifies patterns and relationships among these variables, enabling reliable forecasts. Ultimately, this tool aims to increase productivity, reduce losses, and support sustainable agricultural practices through data-driven insights