Leveraging Soil Nutrient Patterns for Improved Crop Yield Through Random Forest Modeling

Abstract

This research explores the use of Random Forest modeling to improve crop yield predictions by analyzing soil nutrient patterns. Recognizing the critical role that nutrients such as nitrogen, phosphorus, and potassium play in plant growth, the study focuses on identifying complex interactions between soil health and crop productivity that traditional methods often overlook. By collecting and analyzing comprehensive soil data, a Random Forest model is developed to predict crop yields with greater accuracy. The results demonstrate how this machine learning (ML) approach can guide precision agriculture, helping farmers optimize fertilization and soil management to enhance crop yields while promoting more sustainable agricultural practices.