标题： 解码和工程化植物生物群通信以实现智能农业 显示英文标题

标题： Decoding and Engineering the Phytobiome Communication for Smart Agriculture

摘要： 智能农业应用将物联网和机器学习/人工智能（ML/AI）等技术整合到农业中，有望解决日益增长的粮食需求、环境污染和水资源短缺等现代挑战。 随着植物生物组（phytobiome）概念的提出，该概念定义了包括植物、其环境和相关生物在内的区域，以及分子通信（MC）的最新出现，利用通信理论来推进农业科学和实践存在重要机遇。 在本文中，我们旨在使用通信工程的视角，以全面理解植物生物组通信，并弥合植物生物组通信与智能农业之间的差距。 首先，介绍了通过分子和电生理信号进行植物生物组通信的概述，并提出了一个将植物生物组建模为通信网络的多尺度框架。 然后，通过植物实验展示了如何利用该框架对电生理信号进行建模。 此外，还提出了通过工程化植物生物组通信实现的智能农业应用，例如智能灌溉和农药的定向输送。 这些应用将ML/AI方法与由MC支持的生物纳米事物互联网相结合，为更高效、可持续和环保的农业生产铺平道路。 最后，讨论了这些应用的实施挑战、开放的研究问题和工业前景。 显示英文摘要

摘要： 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.