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INFEWS:US-China - Modeling and Determination of Photosynthetic Water Needs and Development of a Smart Irrigation System to Minimize Water and Energy Use in Food Crop Production

INFEWS：中美 - 光合水需求的建模和确定以及智能灌溉系统的开发以最大限度地减少粮食作物生产中的水和能源消耗

基本信息

批准号：
1903716
负责人：
Jinglu Tan
金额：
$ 50万
依托单位：
University of Missouri-Columbia
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2025-03-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1903716&HistoricalAwards=false
关键词：
INFEWS US China Modeling Determination

项目摘要

This project was awarded through the "National Science Foundation (NSF) / National Natural Science Foundation of China (NSFC) Joint Research on Environmental Sustainability Challenges" opportunity. Irrigated food crop production is a major contributor to water shortages in many parts of the US and the world, but a plant-based drought measure for optimized irrigation has been elusive. However, this challenge provides an opportunity to develop new technology to measure plant stress and advance the science of photosynthesis. This research will analyze sun-catalyzed chemical reactions in plants and develop a quantifiable measure of plant stress related to water needs for photosynthesis. Based on these measurements researchers at the University of Missouri-Columbia, in collaboration with researchers at Jiangnan University in China, will develop a smart irrigation system to minimize water and energy use in food crop production. This project will include a technology demonstration in a commercial-scale production facility. Taking advantage of a unique combination of strengths in plant research and intelligent computing offered by this international collaborative research team, this project will result in a useful technology that will reduce the water stress associated with crop production. In addition, the proposed water-stress measurement is a visual tool that provides an entry point of interest for both school children and adults that will allow for an effective demonstration of how plants are affected by irrigation. The goal of this project is to derive a photosynthetically-based measure of water deficiency in plants and then to use this measurement to develop a smart irrigation methodology to minimize water and energy use in food crop production. Researchers at the University of Missouri - Columbia, in collaboration with researchers at Jiangnan University in China, will use light to probe the plant photosynthesis, a process which relies on water as the source of electrons to harvest photoenergy. By studying the photochemical reactions involved, the research team will determine when water is deficient for photosynthesis-driven plant growth. Using mathematical tools, an analysis of the chlorophyll fluorescence emitted from plants will be performed to derive a signature indicative of water deficiency. The signature, which is not obvious without a fundamental analysis of the underlying photochemical reactions, provides a drought stress measure which will be used as the feedback signal to control irrigation. A computer vision system will be devised to measure fluorescence from plant foliage or canopy to permit field use of the technology. Demonstration that the drought measure is correlated with plant growth rate will be accomplished through plant experiments in a controlled greenhouse. Using deep-learning tools to incorporate other factors and information, such as plant type and climate, the research team will augment the applicability and performance of the irrigation control system. Finally, at a commercial production facility, the team will perform a demonstration of this water- and energy-saving technology. This research will generate new knowledge on how the photochemical reactions interactively take place in plants to transport photoelectrons and how a lack of water is manifested in, and thus, can be determined from, the behavior of chlorophyll fluorescence. This will lead to a currently elusive plant-need-based drought stress measure. As a significant step beyond laboratory research, the project will develop and integrate modern technologies in computer vision, sensing, deep learning, and the internet of things in a smart irrigation system. In the course of the project, students and postdoctoral fellows will be trained to fill an important need for researchers versed in engineering analysis of plant processes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

本项目通过“国家自然科学基金（NSF）/国家自然科学基金（NSFC）环境可持续性挑战联合研究”的机会获得资助。灌溉粮食作物生产是美国和世界许多地区水资源短缺的主要原因，但用于优化灌溉的基于植物的干旱措施一直难以捉摸。然而，这一挑战为开发新技术来测量植物胁迫和推进光合作用科学提供了机会。这项研究将分析植物中太阳催化的化学反应，并开发一种与光合作用所需水分相关的植物胁迫的量化措施。根据这些测量结果，密苏里大学哥伦比亚分校的研究人员与中国江南大学的研究人员合作，将开发一种智能灌溉系统，以最大限度地减少粮食作物生产中的水和能源使用。该项目将包括在商业规模生产设施中进行技术演示。利用这个国际合作研究团队在植物研究和智能计算方面的独特优势，该项目将产生一种有用的技术，可以减少与作物生产相关的水分胁迫。此外，拟议的水压力测量是一个直观的工具，为学童和成人提供了一个感兴趣的切入点，可以有效地展示灌溉对植物的影响。该项目的目标是推导出一种基于光合作用的植物缺水测量方法，然后利用这种测量方法开发一种智能灌溉方法，以最大限度地减少粮食作物生产中的水和能源使用。密苏里州-哥伦比亚大学的研究人员与中国江南大学的研究人员合作，将利用光来探测植物的光合作用，这一过程依赖于水作为电子来源来获取光能。通过研究所涉及的光化学反应，研究小组将确定什么时候水分不足，光合作用驱动的植物生长。利用数学工具，将对植物发出的叶绿素荧光进行分析，以得出指示缺水的特征。的签名，这是不明显的基础光化学反应的基本分析，提供了一个干旱胁迫措施，将被用作反馈信号，以控制灌溉。将设计一个计算机视觉系统来测量植物叶子或树冠的荧光，以允许实地使用该技术。干旱措施与植物生长速率相关的证明将通过在受控温室中的植物实验来完成。通过使用深度学习工具来整合其他因素和信息，如植物类型和气候，研究团队将增强灌溉控制系统的适用性和性能。最后，该团队将在商业生产设施中演示这种节水节能技术。这项研究将产生关于光化学反应如何在植物中相互作用以传输光电子以及缺水如何表现在叶绿素荧光行为中的新知识。这将导致目前难以实现的基于植物需求的干旱胁迫措施。作为实验室研究的重要一步，该项目将在智能灌溉系统中开发和集成计算机视觉、传感、深度学习和物联网等现代技术。在项目过程中，学生和博士后研究员将接受培训，以满足精通工厂过程工程分析的研究人员的重要需求。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。