Smart Agriculture Climate Adaptation via AI Big Data
DOI:
https://doi.org/10.5281/zenodo.21412448Keywords:
AI-Enabled Climate Adaptation in Agriculture, Smart Agriculture Big Data Platforms, Cloud-Based Agricultural Decision Systems, Cyber-Physical Systems for Smart Farming, Climate Adaptation Modeling for Agriculture, Agricultural Climate Risk Analytics, Microclimate Assimilation Modeling, Climate Scenario Planning for Agriculture, Data-Driven Agricultural Decision Support, Predictive Analytics for Crop Resilience, AI-Based Agricultural Impact Assessment, Integrated Agricultural Data Ecosystems, Climate-Smart Farming Technologies, Big Data for Sustainable Agriculture.Abstract
Climate change is threatening the future of agriculture worldwide, and significant adaptation efforts will be needed to mitigate its consequences. Although many adaptation strategies have been proposed, a comprehensive climate-adaptation framework for smart agriculture based on AI-enabled big data are still lacking. To bridge this gap, a cloud-based Cyber-Physical Environment for Smart Agricultural Climate Adaptation was developed by integrating big-data sources needed for climate-adaptation simulation, prediction, management, and decision-making. The resulting Information, Data, Process, Software, and Model (IDPSM) ecosystem integrates supervised and unsupervised predictive-analytics models, downscaling and microclimate-assimilation Modeling, and weather-and-microclimate-scenario-planning tools, and supports AI-enabled decision-making.
Several representative Climate Adaptation Case Studies were deployed in various global contexts to demonstrate the use of the IDPSM ecosystem and its associated AI methods for climate-adaptation simulations, impact assessments, and/or decision making. Lessons learned from these case studies illustrate the potential of deploying such an AI-enabled big-data ecosystem to drive data-centric climate adaptation for agriculture.
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