Climate Change and Agricultural Resilience: AI-Based Crop Prediction Models
Abstract
Climate change has disrupted traditional agricultural patterns, making food security a growing concern. This paper examines how artificial intelligence (AI) and machine learning models can predict crop yields, monitor soil health, and optimize irrigation systems to improve agricultural resilience. Using satellite imagery and remote sensing technologies, AI-driven models can provide real-time insights into weather conditions and disease outbreaks. Case studies from China’s smart farming initiatives showcase the effectiveness of precision agriculture in increasing productivity while minimizing environmental impact. The research concludes with recommendations for scaling AI-driven agriculture in developing regions to combat global food insecurity.
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