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.
