AI-Driven Digital Twin Models for Hybrid Crop Selection and Climate-Resilient Yield Prediction

Abstract

Advancements in climate change have launched major hurdles against worldwide agricultural systems which diminish plant production alongside food distribution stability. This paper evaluates the application of AI-based digital twin systems in hybrid crop decisions along with climate-resistant yield estimation. The combination of IoT sensors along with AI algorithms and virtual farm replicas ensures both environmental measurement precision and resource optimization and crop management prediction enhancement. The document presents insights about how digital twin and artificial intelligence tech bring advantages for soil health improvement and pest control and weather prediction. This research demonstrates how machine learning technology chooses specific hybrid species that adapt to multiple climate zones as well as predicts harvest outputs during environmental transformations. The realization of AI benefits depends on solving issues with data precision and system costs and flexibility to make sure all farming levels acquire equal technological opportunity. The paper ends with a discussion about how AI and digital twins represent future potential to built sustainable agricultural systems which resist climate change and provides actionable guidance for farmers together with policymakers seeking improvements in agricultural sustainability and productivity.