AI-driven crop yield prediction based on soil health analysis

This study investigates an AI based model to predict crop yields based on soil health monitoring and increasing agricultural productivity through farming. Soil health (determined by factors such as nutrient levels, pH, and organic matter) plays a significant role in crop growth. However, traditional yield prediction methods are often inaccurate and do not account for the interactions between soil, environment, and crop characteristics. This study used machine learning models to analyze soil and environmental data to capture key variables that affect crop performance. The model integrates information from soil samples, weather patterns, and crop history to provide farmers with information to guide crop management decisions. The findings show that AI-based predictions outperform traditional models, given that the data can be tailored to agriculture. This research has important implications for permaculture, allowing farmers to increase yields and reduce input costs while promoting more environmentally friendly agriculture.