Agriculture Yield Prediction: AI-Driven Optimization for Sustainable Farming

Author(s)	AMOL D. CHOKHAT, APEKSHA R. GADPAYLE, BHUMIKA M. KSHIRSAGAR, SANDEELI P. PAWAR, OM V. GHATE
Country	India
Abstract	India is one of the largest agricultural economies, yet traditional farming methods hinder optimal productivity. Farmers often cultivate the same crops without considering soil health and apply fertilizers without assessing nutrient deficiencies, leading to environmental degradation and lower yields. This paper presents an AI-driven agricultural yield prediction system that utilizes machine learning algorithms to analyze soil quality, weather conditions, and past crop data. By offering precise crop recommendations and resource management strategies, the system enhances productivity and sustainability. Our model leverages Random Forest and Deep Learning techniques, significantly improving accuracy in crop yield forecasting. The study demonstrates that AI-driven analytics can empower farmers with data-driven insights, leading to higher profitability and efficient resource utilization.
Keywords	Keywords: Machine Learning, Crop Prediction, Decision Trees, Support Vector Machine, AI in Agriculture, Sustainable Farming, Deep Learning
Field	Engineering
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-03-20
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.37718
Short DOI	https://doi.org/g8949k

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