Agricultural Adaptation Intelligence: Multi-dimensional Control Theory and Intelligent Evolutionary Framework for Crop-Microbiome-Ecosystem Systems
Driven by accelerating global climate change and frequent extreme weather events, traditional agricultural production systems face unprecedented challenges regarding survival and productivity. This paper systematically proposes a novel interdisciplinary concept termed Agricultural Adaptation Intelligence (AAI). AAI aims to explore how crops, microorganisms, and agricultural ecosystems achieve stress survival and maximize productivity through multi-scale sensing, dynamic feedback, autonomous learning, and evolutionary adaptation. We construct a fundamental quad-loop framework based on Sensing-Feedback-Learning-Adaptation (SFLA) and elaborate on the theoretical mechanisms of crop phenotypic plasticity, rhizosphere microbial co-evolution, and agro-ecosystem self-organization. By introducing plain-text system dynamics equations and fitness evaluation models, this paper clarifies the coupling mechanisms of information flow and energy flow across different biological scales. Furthermore, we discuss paths for integrating artificial technology with natural biological intelligence, proposing an evolutionary prediction framework empowered by artificial intelligence and digital twin technologies. Agricultural Adaptation Intelligence provides not only a solid theoretical underpinning for understanding how crops and ecosystems intelligently cope with adversity, but also a transformative direction for next-generation stress-resilient crop breeding, smart agro-ecosystem management, and sustainable agricultural evolution.