用通信工程视角解析植物微生态对话,推动智慧农业发展
Decoding and Engineering the Phytobiome Communication for Smart Agriculture
- 将植物与环境的分子/电生理信号建模为通信网络
- 通过实验验证电生理信号在多尺度通信框架中的可建模性
- 提出智能灌溉与精准施药等应用场景,适合农业智能化研究者
智慧农业融合物联网与机器学习/人工智能技术,有望应对粮食需求增长、环境污染和水资源短缺等挑战。结合植物生物组(phytobiome)概念——即植物、其环境及共生生物的统一体——以及分子通信(MC)的兴起,利用通信理论推进农业科学与实践具有重要机遇。本文提出从通信工程视角理解植物生物组通信,并弥合其与智慧农业之间的鸿沟。首先综述了通过分子与电生理信号实现的植物生物组通信,构建了将植物生物组视为通信网络的多尺度框架。随后通过植物实验展示了该框架对电生理信号的建模能力。进一步提出了基于分子通信的智能灌溉与农化品靶向递送等智慧农业应用,这些应用融合了机器学习/人工智能与由分子通信驱动的生物-纳米-事物互联网(Internet of Bio-Nano-Things),为更高效、可持续、环保的农业生产铺平道路。最后讨论了实现中的挑战、开放研究问题及产业前景。
原文摘要 · Abstract (English)
Smart agriculture applications, integrating technologies like the Internet of Things and machine learning/artificial intelligence (ML/AI) into agriculture, hold promise to address modern challenges of rising food demand, environmental pollution, and water scarcity. Alongside the concept of the phytobiome, which defines the area including the plant, its environment, and associated organisms, and the recent emergence of molecular communication (MC), there exists an important opportunity to advance agricultural science and practice using communication theory. In this article, we motivate to use the communication engineering perspective for developing a holistic understanding of the phytobiome communication and bridge the gap between the phytobiome communication and smart agriculture. Firstly, an overview of phytobiome communication via molecular and electrophysiological signals is presented and a multi-scale framework modeling the phytobiome as a communication network is conceptualized. Then, how this framework is used to model electrophysiological signals is demonstrated with plant experiments. Furthermore, possible smart agriculture applications, such as smart irrigation and targeted delivery of agrochemicals, through engineering the phytobiome communication are proposed. These applications merge ML/AI methods with the Internet of Bio-Nano-Things enabled by MC and pave the way towards more efficient, sustainable, and eco-friendly agricultural production. Finally, the implementation challenges, open research issues, and industrial outlook for these applications are discussed.
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