• Ms.Archana K
• Dr. Shashidhar Kini K

This project proposes a data-driven agriculture yield prediction model that leverages historical agricultural data, weather conditions, soil parameters, and crop-specific features to forecast productivity. By applying machine learning algorithms such as Random Forest and Decision Tree, the model identifies critical patterns and relationships influencing yield outcomes. The goal is to support farmers and agricultural stakeholders with accurate, timely insights for informed decision-making, optimized resource allocation, and improved food security through smarter, technology-driven farming practices.

Agriculture yield prediction, Machine learning, Gradient Descent, Random Forest, Weather data, Soil parameters, Data-driven farming, Precision agriculture, Predictive modeling

"DATA-DRIVEN AGRICULTURE YIELD PREDICTION MODEL", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.1163-1166, July-2025, Available :http://www.jetir.org/papers/JETIRGX06224.pdf

"DATA-DRIVEN AGRICULTURE YIELD PREDICTION MODEL", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp1163-1166, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06224.pdf