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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-24Cited by 0

Theoretical Framework, Perception Mechanisms, and Application Prospects of Crop Resource Perception Science in Smart Agriculture

Jincheng Zhang

With the intensifying pressure of global climate change and resource constraints, the transition of traditional agriculture toward highly efficient, precise, and sustainable smart agriculture has become an inevitable trend. As the absolute core of agricultural production, crops possess inherent resource perception capabilities that directly determine their resource use efficiency and stress adaptation strategies. This paper systematically elicits the theoretical foundation, core research domains, and application prospects of Crop Resource Perception Science, an emerging interdisciplinary field. Crop Resource Perception Science aims to decipher how crops precisely perceive key environmental signals—such as water, nitrogen, light, salinity, and neighbor competition pressure—via receptor systems at the molecular, cellular, and organ levels, integrate these signals, and conduct long-distance signaling to formulate corresponding growth strategies involving morphological plasticity, metabolic reprogramming, and physiological regulation. This paper thoroughly explores the molecular receptor and signal transduction mechanisms involved in sensing water, salt, nitrogen, light environments, and spatial competition pressure. By integrating the perspective of plant neurobiology, it examines the long-distance transmission networks of trans-membrane electrical signals and calcium waves, while establishing quantitative characterization models for crop resource perception and growth strategy conversion. Furthermore, this study elaborates on the deep integration pathways between Crop Resource Perception Science and smart agriculture, including the development of micro in-situ sensors, the reconstruction of perception-driven crop digital twin systems, and precision decision-making applications for intelligent farm machinery. Finally, the paper analyzes the key theoretical and technological bottlenecks currently facing the field and provides insights into future developmental directions.

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