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

本项目获国家科学基金/国家自然科学基金中国(国家自然科学基金委员会)环境可持续性挑战联合研究项目资助。灌溉粮食作物生产是美国和世界许多地区缺水的主要原因，但优化灌溉的植物干旱措施一直难以找到。然而，这一挑战为开发测量植物逆境的新技术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可量化测量方法。在这些测量的基础上，密苏里哥伦比亚大学的研究人员将与位于中国的江南大学的研究人员合作，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消耗。该项目将包括在商业规模的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组合，该项目将产生一项有用的技术，将减少与作物生产相关的水分压力。此外，拟议的水分压力测量是一种可视化工具，为在校儿童和成年人提供了一个感兴趣的切入点，使其能够有效地演示植物如何受到灌溉的影响。该项目的目标是得出一种基于光合作用的植物缺水测量方法，然后利用该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消耗。密苏里哥伦比亚大学的研究人员将与位于中国的江南大学的研究人员合作，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。通过研究涉及的光化学反应，研究小组将确定何时水分不足，不利于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，以得出指示缺水的信号。如果不对潜在的光化学反应进行基本分析，这一特征就不明显，它提供了一种干旱胁迫措施，将被用作控制灌溉的反馈信号。将设计一种计算机视觉系统来测量植物树叶或树冠的荧光，以便在野外使用这项技术。干旱措施与植物生长速度的相关性将通过受控温室的植物实验来完成。使用深度学习工具来纳入其他因素和信息，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，在一个商业生产设施中，该团队将展示这项节水和节能技术。这项研究将产生新的知识，如何在植物中相互作用地发生光化学反应来传输光电子，以及如何在叶绿素荧光行为中表现出缺水，从而可以确定缺水。这将导致目前难以捉摸的以植物需求为基础的干旱胁迫措施。作为实验室研究之外的重要一步，该项目将在智能灌溉系统中开发和整合计算机视觉、传感、深度学习和物联网方面的现代技术。在项目过程中，学生和博士后研究员将接受培训，以满足对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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.