ERI: Crop-FIT: Technology to Support Integrated Wearable Fitness Trackers for Plants

ERI：Crop-FIT：支持植物集成可穿戴健身追踪器的技术

• 批准号: 2138701
• 负责人: Shawana Tabassum
• 金额: $ 20万
• 依托单位: University of Texas at Tyler
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138701&HistoricalAwards=false
• 关键词: ERI; Crop; FIT; Technology; Support

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).The most promising strategy for producing enough food for humans and livestock in the future is to make farms more efficient, profitable, and sustainable in their use of nonrenewable resources. Studies report that drought stress alone yields up to 21% and 40% of global reductions in wheat and maize productions, respectively. In addition, agriculture accounts for 55 to 60% of methane emissions and 21 to 25% of carbon-di-oxide emissions globally, thus poorly planned and unsustainable agriculture practices contribute substantially to global warming. Lack of real-time monitoring capabilities of crop health remains one of the central limiting factors for simultaneously mitigating productivity losses and adverse environmental impacts of agriculture practices. There has been a significant thrust in the development of wearable medical devices. However, their applications remain heavily unexplored in plants. Toward this end, this project will develop new crop-wearable technologies that can monitor the levels of plant hormones (which denote a plant’s first response to its environment), as well as record plant tissue remodeling under environmental stressors (e.g., drought, heat, and salinity stresses). This technology could be a pivotal tool in precision farming enabling real-time tracking of the fitness of plants, with direct benefit to the agricultural society. The findings from this project will aid in engineering higher-performing, stress-tolerant crop cultivars and setting up on-demand irrigation schedules in the long run, thus preventing the over-application of nonrenewable agrochemicals while simultaneously increasing the production. This project will also train students from minority backgrounds and the producer community on the productivity advantages of sensors-driven precision farming.This research aims to design, fabricate, and validate integrated, in-situ plant sensors for catalyzing the next generation of crop engineering and precision farming. Real-time monitoring of crop parameters is crucial for implementing immediate interventions to mitigate productivity losses and adverse environmental impacts. Sensors for precision farming applications are limited to indirectly estimating crop needs and health issues from weather conditions, soil properties, or aerial imagery that do not provide chemical profiling in plants, thereby lacking information on the onset and progression of crop health conditions. Hence, there is a significant gap in knowledge regarding precisely and directly quantifying plant needs and their responses to environmental conditions. This research proposes a holistic solution to this problem by developing an integrated, multiplexed stem sensor for in-situ and quantitative profiling of four key phytohormones/secondary metabolites, which denote a plant’s first response to its environment. The device is comprised of an array of electrochemical sensors, integral microfluidics, and data processing in an embedded platform for in-situ collection and monitoring of phytohormones in sap. The sensor will be validated with maize grown under abiotic stress conditions to (1) analyze the sensor’s sensitivity, selectivity, robustness, and impact on plant growth, (2) elucidate the dynamic correlations between the phytohormones under environmental stress conditions through multiplexed sensing, and (3) harness these data streams to differentiate the impact of heat/drought/salinity stresses on plants. The second thrust of this research is to design and simulate a photonic crystal-based fiber-optic bundle for use in real-time root endoscopy and spectroscopy in living plants. This first-of-its-kind fiber bundle will be pivotal in imaging root zone and monitoring root exudate metabolites in real-time. The combined analysis of tissue-specific (shoot versus root) metabolites and deciphering the dynamic tissue remodeling will enhance our fundamental understanding of metabolite gradients across the whole plant, real-time adaptation of roots to stressors (questions that remain unexplored in previous studies) and illuminate a pathway for new crop breeding strategies.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.

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。未来要为人类和牲畜生产足够的食物，最有希望的策略是使农场在使用不可再生资源方面更有效率、更有利可图、更可持续。研究报告称，仅干旱压力就分别造成了全球小麦和玉米产量减少的21%和40%。此外，农业占全球甲烷排放量的55%至60%，占二氧化碳排放量的21%至25%，因此，规划不周和不可持续的农业做法是全球变暖的主要原因。缺乏作物健康的实时监测能力仍然是同时减轻农业做法的生产力损失和不利环境影响的主要限制因素之一。可穿戴医疗设备的发展已经有了很大的推动力。然而，它们在植物中的应用仍未得到充分探索。为此，该项目将开发新的作物可穿戴技术，可以监测植物激素水平（这表示植物对环境的第一反应），并记录环境胁迫（如干旱、高温和盐度胁迫）下的植物组织重塑。这项技术可以成为精准农业的关键工具，实现对植物适应性的实时跟踪，直接造福农业社会。该项目的研究结果将有助于设计出性能更高、耐胁迫的作物品种，并从长远来看建立按需灌溉计划，从而在提高产量的同时防止不可再生农用化学品的过度使用。该项目还将培训来自少数民族背景的学生和生产者社区，让他们了解传感器驱动的精准农业的生产力优势。本研究旨在设计、制造和验证集成的原位植物传感器，以催化下一代作物工程和精准农业。实时监测作物参数对于实施即时干预措施以减轻生产力损失和不利的环境影响至关重要。用于精准农业应用的传感器仅限于从天气条件、土壤性质或航空图像中间接估计作物需求和健康问题，这些图像不能提供植物的化学特征，因此缺乏关于作物健康状况发生和发展的信息。因此，关于精确和直接量化植物需求及其对环境条件的反应的知识存在重大差距。本研究提出了一个整体的解决方案，通过开发一个集成的，多路的茎传感器，用于原位和定量分析四种关键的植物激素/次生代谢物，这表示植物对其环境的第一反应。该装置由一组电化学传感器、集成微流体和数据处理组成，在嵌入式平台中用于原位收集和监测汁液中的植物激素。该传感器将在非生物胁迫条件下生长的玉米上进行验证，以(1)分析传感器的灵敏度、选择性、鲁棒性和对植物生长的影响，(2)通过多路传感阐明环境胁迫条件下植物激素之间的动态相关性。(3)利用这些数据流来区分热/干旱/盐度胁迫对植物的影响。本研究的第二个重点是设计和模拟一个基于光子晶体的光纤束，用于活体植物的实时根内窥镜和光谱学。这种首创的纤维束将在根区成像和实时监测根渗出代谢物方面发挥关键作用。结合组织特异性（茎对根）代谢物的分析和对动态组织重塑的解读，将增强我们对整个植物代谢物梯度的基本理解，根系对胁迫源的实时适应（以前的研究中尚未探索的问题），并为新的作物育种策略提供途径。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

Real-time quantification of salicylic acid with a fiber optic sensor functionalized by gold nanoparticles-copper metal organic conjugate coating

• DOI: 10.1117/12.2605711
• 发表时间: 2022-06
• 作者: Shawana Tabassum

Fruit-FIT: Drone Interfaced Multiplexed Sensor Suite to Determine the Fruit Ripeness

Fruit-FIT：无人机连接多路传感器套件以确定水果成熟度

• DOI: 10.1109/sensors52175.2022.9967097
• 发表时间: 2022
• 期刊: 2022 IEEE Sensors
• 作者: Hossain, Nafize Ishtiaque;Tabassum, Shawana
• 通讯作者: Tabassum, Shawana

A microneedle-based Leaf Patch with IoT Integration for Real-time Monitoring of Salinity Stress in Plants

基于微针的叶贴与物联网集成，用于实时监测植物盐度胁迫

• DOI: 10.1109/dcas53974.2022.9845643
• 发表时间: 2022
• 期刊: 2022 IEEE 15th Dallas Circuit And System Conference (DCAS
• 作者: Galvan, Carlos;Montiel, Rudy;Lorenz, Karl;Carter, Jared;Hossain, Nafize Ishtiaque;Tabassum, Shawana
• 通讯作者: Tabassum, Shawana

Design and Development of 3D Printed Stem Stem-Mounted Microneedle Microneedle- Based Platform for Multiplexed Monitoring of Phytohormones in Live Plants

3D 打印茎杆安装微针平台的设计和开发，用于活体植物中植物激素的多重监测

• 发表时间: 2022
• 期刊: LSAMP Summer Internship
• 作者: Bruton, William;Ahmadi, Alisina Ahmadi;Hossain, Nafize I.;Tabassum, Shawana
• 通讯作者: Tabassum, Shawana

Performance Analysis of a Kirigami-shaped Temperature Sensor

• DOI: 10.1109/metrocon56047.2022.9971137
• 发表时间: 2022-11
• 期刊: 2022 IEEE MetroCon
• 作者: N. I. Hossain;Shawana Tabassum