Predicting Crop Resilience to Climate Change with Machine Learning: Insights for Sustainable Agriculture
Abstract
Agricultural systems are increasingly vulnerable to the impacts of climate change, requiring innovative approaches to enhance crop resilience. This study presents a machine learning framework that predicts crop performance under varying climate conditions, using historical data on crop yields, weather patterns, and soil characteristics. Our model identifies crop varieties and planting strategies best suited to withstand droughts, extreme temperatures, and other climate-related stressors. The findings support adaptive agricultural practices that maintain productivity, reduce crop failure, and promote food security, paving the way for more resilient and sustainable farming in a changing climate.
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