Abstract
In this project, we seek to create a closed-loop system for managing manure of grazing livestock. To accomplish this goal, we will use a autonomous vehicle platform which will work collaboratively with sensors on livestock in the field. Livestock sensors will flag where and when animal defecate and urinate within the field and send this information to the autonomous vehicle. The vehicle will then plan and execute a manure management strategy based on the location of manure in the field and sensed data describing the moisture and nutrient composition of the pasture soil. Manure management options will include moving manure to different areas of the field that have more faborable nutrient composition or are less hydrologically sensitive; tiling manure into the soil to prevent surface runoff; and removing manure from the field entirely. Through these management options, they system will precision-manage the nutrient composition of soil to optimize manure value as fertilizer and minimize environmental impacts. In order to work toward this vision, we will also conduct a number of addiitonal tests, including evaluating the animal-robot interactions within the field; leveraging novel simulation platforms to efficiently train autoonomous control approaches; and development work to improve precision and accuracy of sensors to detect soil nutrient compostion. Collectively, these investigations will contribute to our efforts to generate the resultant closed-loop system which we will then demonstrate on research and working farms.
Robin White
Performance Period: 07/01/2021 - 06/30/2024
Institution: Virginia Polytechnic Institute and State University
Sponsor: USDA
Award Number: 2038663
Abstract
In this project, we empass recent advances in wastewater treatment, food security, data analytics, and machine intelligence.We propose to study novel optimized technology-driven controlled-environment agriculture (CEA) systems that can achieve high-areal vegetable productivity to increase food and nutritional security of urban communities with low operating cost and reduced energy consumption.Our project focuses on two core CPS research areas, i.e., control and data analytics, inspired by the design and operation of a Pilot-Testbed at Georgia Tech to couple the water and nutrients in domestic wastewater (DWW) to high-productivity CEAs. Food production in urban CEAs must overcome land acquisition constraints, requiring DWW-CEA operating cost reductions accompanied by increased productivity and nutrition. However, it is highly challenging to control and optimize this complex system of subsystems. In our case, we need to coordinate the Pilot-Plant and Pilot-Farm, examine their inter-correlation, and support dynamic and robust optimal decisions to achieve the highest yield, while simultaneously satisfying various performance specifications, i.e., nutrient composition, operating cost and energy consumption, with the guarantee that food safety requirements are met. Moreover, the profound impact of numerous operating conditions and parameters on vegetable phenotype, yield and nutrient composition during different growth periods needs to be thoroughly understood. We must progress from model-driven CPS fundamentals to an integrated data-driven model-based approach.
zhaohui tong
Dr. Zhaohui Tong is currently an associate professor in the School of Chemical and Biomolecular Engineering at the Georgia Institute of Technology. She received a B.S. degree in Chemical Engineering from Changsha University of Science and Technology in China. She earned her first M.S. degree in Chemical Engineering, with a concentration in biopolymer synthesis at Tianjin University of Science and Technology in China. Under the guidance of Dr. Yulin Deng, she earned her Ph. D. and second M.S. degree in Chemical Engineering with a concentration in organic-inorganic nanocomposite synthesis, from the Georgia Institute of Technology in 2007. She had worked as a consulting engineer in the Energy and Chemical Division of Ch2mHill Engineering Ltd. for two years. In 2010 she joined the Agricultural and Biological Engineering Department at the University of Florida as an Assistant Professor in the Biological Engineering program and was promoted the associate professor in 2017. Tong’s research interests include the conversion of renewable resources to bioproducts (biochemicals, biomaterials, and biofuels), bioprocessing, sustainable process control, and modeling.
Performance Period: 10/01/2022 - 05/31/2024
Institution: Georgia Institute of Technology
Sponsor: USDA
Award Number: 2020-67021-38586
Abstract
This has the specific goal of significantly shortening the time to measure apple fruitlet growth, which will enable apple growers to manage crop load more precisely and efficiently. The technology developments used to do this will make fundamental advances in robotics and sensing for agriculture, and will take important steps toward using intelligent robots as labor saving devices in specialty crop production. The project will train graduate and undergraduate students in plant science and computer science, giving them important cross-disciplinary research experiences early in their careers. Ideas from the research project will be incorporated into a popular K-12 program that focuses on providing girls and young women with hands-on activities in robotics and agricultural science.1. Develop a 3-D camera, a robotic arm, and robot that and related software that can identify and measure fruit clusters, producing data for use in the current apple fruit thinning model MaluSim.2. Develop a cell phone application that can be used to measure fruit clusters, producing data for use in the current apple fruit thinning model MaluSim.3. Combine recent advances in 2D semantic image segmentation with 3D computer vision and mapping techniques to create robust perception systems that work in the unstructured clutter common in agricultural environments.4. Investigate the use of deep reinforcement learning approaches to solve manipulation tasks in these environments, and, specifically, it will explore the use of auxiliary reward functions to speed up the training process and transfer the result into real-world applications.5. Develop a template for development of similar imaging methods in other crop systems, solving other management problems where rapid, accurate measurement are required to more precisely manage inputs.6. Work with graduate, undergraduate and K-12 students showing the value of interdisciplinary work in agriculture and computer science.
Daniel Cooley
Performance Period: 06/01/2020 - 05/31/2024
Institution: University of Massachusetts Amherst
Sponsor: USDA
Award Number: 1931913
Abstract
Commercial apple growers must remove a portion of new apple fruitlets every year in order maintain fruit size, and prevent trees from develop a pattern of alternative year bearing, where some years there are many fruit, and other years very few. The challenge for a commercial grower is to remove a number fruitlets in the first three to four weeks after they form so as to optimize size and number of harvested fruit later in the year. To do this, commercial growers spray chemicals, generally two to four applications per year. The impact of a chemical thinner depends on weather and other factors. Thinning has historically been as much art as science, and growers often remove too few fruit, or occasionally, too many. In either case, production and profitability are less than they might be.Recently, horticultural researchers have developed a way for growers to more precisely determine if and when to apply chemical thinners. Basically, it requires that the new fruitlets be measured to see how fast they are growing. Some grow more slowly than others, or not at all, and these are the fruitlets that will drop. The method requires that the same fruitlets be measured two or more times over a two- to three-week period, and that many fruitlets be evaluated for each apple variety and block. The measurements are done by hand, with calipers, and each measurement has to be carefully recorded. While it makes thinning much more accurate, it takes a great deal of time, and growers are reluctant to do it.Computer technology in the form of smart-phones or robotically manipulated cameras offer a solution. This project will use pictures taken with phones and other cameras as a substitute for caliper measurements. Initially, many images will be used to build a measuring program using artificial intelligence. It's a challenge, as the computer has to pick out small apple fruitlets from among leaves, branches and other objects. It then has to determine how big the fruitlet is, and finally, make sure that measurements are taken from the same fruitlets when needed. However, if successful, growers will be able to quickly evaluate the need to thin using a set of cell-phone photos. This in turn, should improve production, and potentially reduce chemical use.
George Kantor
Performance Period: 06/01/2020 - 05/31/2024
Institution: Carnegie Mellon University
Sponsor: USDA
Award Number: 1932498
Abstract
High throughput field phenotyping is a relatively new but rapidly growing research area, and it will remain a top agricultural research priority in the next decade. Remote sensing technologies, proximal sensors, platforms such as unmanned aerial vehicles (UAVs) and ground vehicles, and statistical data-driven analytics are being rapidly customized and deployed for high throughput phenotyping and used as plant performance measurement tools for crop improvement/breeding and precision agriculture systems for agronomy, soil science, and farm management. However, high costs, weather-dependent data collection (e.g., human-operated UAV's), data processing lag from complicated and/or inefficient analysis procedures, and a lack of standardization in sensor-based technologies are just a few of the recurring issues preventing these technologies from being more accessible. Additionally, each newly developed phenotyping technology or tool can measure only one or a few facets of highly quantitative and multi-variable traits in agriculture, such as yield, environmental stressors, or drought resistance.Therefore, the loop needed to make concrete advances in improving our food, fuel, and feed crops remains open with the current agricultural technology platforms. Here, we aim to close the loop by developing and deploying an integrated cyber-physical system for connecting plant phenotypes to genotypes with real-time crop management. With a robust wireless environmental sensor network, this integrated cyber-physical system, or "FieldDock", will deploy and manage daily UAV flights over target fields to automate crop modeling and genetic mapping to accelerate breeding efforts for energy efficient, nutritious, and high-yielding crops while tracking farm inputs to potentially guide crop management.Integrated cyber-physical systems like the proposed FieldDock are vital so that high throughput phenotyping tools are streamlined to be accessible for broad and applied agricultural use. With onboard GWAS and crop model processing, researchers will receive a constant stream of remote data that will allow them to focus on analysis and breeding strategies, rather than manually collecting data throughout the growing season. Breeding efforts across the country, both private and academic, employing the minds of many talented researchers and computer engineers could further fine-tune such a device for many different environments within an ever-changing climate. A standardized all-in-one platform like FieldDock could potentially unify global efforts to accelerate some of the most critical breeding goals of our time by making it affordable and lowering the barrier to entry for such a high end, advanced cyber-physical technology.For farmers, the FieldDock platform aims to connect spatial, temporal and multi-layered environmental data in real time while generating powerful predictive analytics and machine learning models that will drive reliable commands to automate field equipment throughout the growing season. A cyber-physical farm will self-learn with such a system in place and adapt to keep pace with the rapidly changing climate and the unpredictable challenges it will bring. FieldDock will act as an all-encompassing platform to gather all crucial field data needed to offer decision support for farmers in the short term while developing machine learning models from detailed datasets for the autonomous farm of the future.Ultimately, the proposed project will collect plot level data at a spatial and temporal resolution necessary for researchers and growers to develop and improve high-yielding, energy efficient crops that are resilient to variable climates, and also benchmark an integrated closed-loop smart farm system that can help agricultural growers reduce their energy inputs in real time.
Nadia Shakoor
Performance Period: 06/15/2020 - 06/14/2024
Institution: Donald Danforth Plant Science Center
Sponsor: USDA
Award Number: 1932569
Abstract
Water and nitrogen represent two of the most expensive inputs to agricultural systems, and two of the critical constraints on overall agricultural productivity. Today, farmers generally over apply nitrogen fertilizer, because the potential cost of over application is less than the potential cost of achieving suboptimal yields. Similarly, in farm settings water is often over applied, particularly when studies are conducted at high resolution within individual center-pivot fields. We willdesignand validatean integrated cyber-physical system to collect and integrate data from remote sensing and low-cost field deployed wearable sensors and use machine learningand mathematical modeling to guide precision water and nutrient interventions in farmer's fields. This would mean that agricultural productivity can be sustained or increased while reducing overall nitrogen fertilizer and irrigation applications. Among the many beneficial effects to society as a whole would be 1) a decrease the environmental impact of agriculture; 2) decreased competition for scarce water supplies between agriculture and growing urban centers; and 3) increased farmer profitability, improving the economic viability of rural economies.The CPS will enable fusion of a large volume of spatio-temporally distributed multi-modal information to create a data-driven decision support platform that provides actionable information on optimal agricultural managementstrategies.The team will continue to leverage and develop extensive outreach and educational activities to train the next generation of scientists, through many existing STEM programs in Iowa State University and University of Nebraska-Lincoln.
Liang Dong
Liang Dong is an associate professor of electrical and computer engineering at Baylor University. His research interests include Digital Communications and Signal Processing, Green Wireless Networks, Cyber-Physical System and Security, Social Internet of Things, and E-health Applications.
Liang Dong is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), a member of the American Physical Society (APS), and a member of the American Society for Engineering Education (ASEE). He served on the executive board of IEEE West Michigan Section from 2006 to 2011 and the executive board of ASEE North Central Section from 2007 to 2008. He also served as a TPC member for IEEE HealthCom 2015, IEEE GlobalSIP 2015 and IEEE GlobalSIP 2016, and a session chair for IEEE WCNC 2013 and IEEE GlobalSIP 2016. He is a member of Sigma Xi, Phi Kappa Phi, and Tau Beta Pi, and a faculty advisor of Eta Kappa Nu.
Performance Period: 06/01/2020 - 05/31/2023
Institution: Iowa State University
Sponsor: USDA
Award Number: 1932428
Abstract
Water and nitrogen represent two of the most expensive inputs to agricultural systems, and two of the critical constraints on overall agricultural productivity. Today, farmers generally over apply nitrogen fertilizer, because the potential cost of over application is less than the potential cost of achieving suboptimal yields. Similarly, in farm settings water is often over applied, particularly when studies are conducted at high resolution within individual center-pivot fields. We willdesignand validatean integrated cyber-physical system to collect and integrate data from remote sensing and low-cost field deployed wearable sensors and use machine learningand mathematical modeling to guide precision water and nutrient interventions in farmer's fields. This would mean that agricultural productivity can be sustained or increased while reducing overall nitrogen fertilizer and irrigation applications. Among the many beneficial effects to society as a whole would be 1) a decrease the environmental impact of agriculture; 2) decreased competition for scarce water supplies between agriculture and growing urban centers; and 3) increased farmer profitability, improving the economic viability of rural economies.The CPS will enable fusion of a large volume of spatio-temporally distributed multi-modal information to create a data-driven decision support platform that provides actionable information on optimal agricultural managementstrategies.The team will continue to leverage and develop extensive outreach and educational activities to train the next generation of scientists, through many existing STEM programs in Iowa State University and University of Nebraska-Lincoln.
Liang Dong
Liang Dong is an associate professor of electrical and computer engineering at Baylor University. His research interests include Digital Communications and Signal Processing, Green Wireless Networks, Cyber-Physical System and Security, Social Internet of Things, and E-health Applications.
Liang Dong is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), a member of the American Physical Society (APS), and a member of the American Society for Engineering Education (ASEE). He served on the executive board of IEEE West Michigan Section from 2006 to 2011 and the executive board of ASEE North Central Section from 2007 to 2008. He also served as a TPC member for IEEE HealthCom 2015, IEEE GlobalSIP 2015 and IEEE GlobalSIP 2016, and a session chair for IEEE WCNC 2013 and IEEE GlobalSIP 2016. He is a member of Sigma Xi, Phi Kappa Phi, and Tau Beta Pi, and a faculty advisor of Eta Kappa Nu.
Performance Period: 06/01/2020 - 05/31/2023
Institution: Iowa State University
Sponsor: USDA
Award Number: 1932554
Abstract
Harnessing recent progresses on wastewater treatment, food security, data analytics, and machine intelligence, we propose to study novel optimized technology-driven controlled-environment agriculture (CEA) systems that can achieve high areal vegetable productivity to increase the food and nutritional security in urban areas with low operating cost and reduced energy consumption.Our project focuses on two core CPS research areas, i.e., control and data analytics, inspired by the design and operations of a pilot testbed at Georgia Tech for coupling the water and nutrients in domestic wastewater (DWW) to high-productivity CEAs. Food production in the CEAs must warrant the high cost of land in urban areas, which makes it necessary to reduce the total DWW-CEA operating cost and increase productivity. However, it is highly challenging to control and optimize this complex system of subsystems. In our case, we need to coordinate the Pilot-Plant and Pilot-Farm, examine their inter-correlation, and support dynamic and robust optimal decisions to achieve the highest production yield, while simultaneously satisfying various performance specifications on nutrient compositions, operating cost and energy consumption, and meeting other safety requirements of DWW-CEAs. Moreover, the profound impact of numerous operating conditions and parameters on the vegetable phenotype, yield and nutrient compositions during different growth periods need to be thoroughly understood. We have to move from model-driven CPS fundamentals to an integrated data-driven model-based approach.
The objective of this 3-year interdisciplinary project is to maximize food productivity and nutrition while minimizing cost, energy and waste. The research is organized as four thrusts, each objective-driven and delineated as follows.InThrust 1, first-principles water models are adapted to the physical system and calibrated before on-site experimental validation. As first-principles models are not available for hydroponic agriculture, inThrust 2we adopt a hybrid approach where parameter-dependent biochemical reactions are supported by machine-learning algorithms with the purpose of parameter estimation and capture of possible un-modeled dynamics. A distinguishing feature ofThrust 2is the implementation of non-invasive spectroscopy and imaging to gain additional plant morphology and nutrition information. The subsystems (e.g., water and hydroponics) are then integrated as a system-wide model and validated experimentally. InThrust 3, we devise control algorithms with the purpose of achieving desired closed-loop performance despite the presence of disturbances and/or uncertain dynamics. As a part ofThrust 4, the resulting CPS is implemented in simulation software and experimentally validated to determine a feasible deployment scale. We expect that our novel, foundational research contributions will be immediately transferable to the water, waste, agriculture and remote sensing industries, but benefit other complex system control applications entirely.
George Lan
Performance Period: 06/01/2020 - 05/31/2024
Institution: Georgia Institute of Technology
Sponsor: USDA
Award Number: 1931919
Abstract
The goal of this project is to use proprioception, localized sensing, and observed forces to develop robust, autonomous fruit picking methods. Fresh market tree fruit growers still rely on a large seasonal labor force for harvesting operations. Despite extensive research over the past thirty years, robotic harvesters are not yet commercially available. Prior work has considered manipulation a robot position control problem, disregarding the need for sensor input after physical contact with the fruit. However, when picking fruit such as apples and pears, professional pickers use active perception, incorporating both visual and tactile input about fruit orientation, stem location, and the fruit's immediate surroundings. We propose to embrace this physical contact by incorporating a rich set of in-hand sensors in an extended manipulation feedback loop with the goal of providing fine control over how the fruit is separated from the tree. To overcome the constraints of data collection in the field, we will develop a learning framework for compartmentalizing the tasks and design an instrumented proxy to serve as a training environment.While our primary focus in this project is fresh market apple and pear harvesting, we believe that this framework will be useful for numerous other agricultural applications that involve physical manipulation. For example, harvesting methods used for greenhouse sweet peppers and tomatoes are highly dependent on knowledge of peduncle orientation. However, automating production has been difficult due to similar challenges with occlusions and determining crop orientation. Another potential area of application for this learning framework is plant phenotyping, using soft tactile sensors, in addition to other sensor types, to measure a plant's physical properties.
Joseph Davidson
Performance Period: 06/01/2020 - 05/31/2024
Institution: Oregon State University
Sponsor: USDA
Award Number: 1932205
Abstract
The potential economic and societal impacts of realizing fully autonomous cyber-physical systems (CPS) are astounding. If the Federal Aviation Administration (FAA) allows integration of unmanned aerial vehicles (UAVs) into the national civilian airspace, the private-sector drone industry is estimated to generate more than 100K high-paying technical jobs over a ten-year span and contribute $82B to the U.S. economy. Self-driving cars are predicted to annually prevent 5M accidents and 2M injuries, conserve 7B liters of fuel, and save 30K lives and $190B in healthcare costs associated with accidents in the U.S. Successful mission pursuit of such fully autonomous CPS hinges on possessing full situational awareness including precise knowledge of its own location. Current CPS are far from possessing this capability, particularly in dynamic, uncertain, poorly modeled environments where GPS coverage may be spotty, obscured, or otherwise impaired. This necessitates developing a coherent analytical foundation to deal with this emerging class of CPS, in which situational awareness and mission planning and execution are intertwined and must be considered simultaneously to address uncertainty, model mismatch, and compensate for potential GPS coverage gaps.
This project is has four main objectives: (1) Analyze the observability of unknown dynamic, stochastic environments comprising multiple agents. This analysis will establish the minimum a priori knowledge needed about the environment and/or agents for stochastic observability. (2) Develop adaptation strategies to refine the agents models of the environment, on-the-fly, as the agents build spatiotemporal maps. Adaptation is crucial, since it is impractical to assume that agents have high-fidelity models describing the environment. (3) Design optimal, computationally efficient information fusion algorithms with performance guarantees. These algorithms will consider physically realistic nonlinear dynamics and observations with colored, non-Gaussian noise, commonly encountered in CPS. (4) Synthesize optimal, real-time decision making strategies to balance the potentially conflicting objectives of information gathering and mission fulfillment. This investigation will enable autonomous CPS to navigate complex tradeoffs, leading to autonomous identification and adoption of the optimal strategy.
This research has far-reaching impact- it will evolve autonomous CPS from merely sensing the environment to making sense of the environment, bringing new capabilities in environments where direct human control is not physically or economically possible. The project has a vertically-integrated education plan spanning K-12, undergraduate, and graduate students. The project will engage economically disadvantaged middle and high school students in the same UAV testbed used for research verification. Also, research outcomes will be infused into new and existing undergraduate and graduate courses.
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.
Zak Kassas
Performance Period: 10/01/2022 - 03/31/2024
Institution: Ohio State University
Sponsor: NSF
Award Number: 2240512