2024 CPS RISING STARS Workshop
The 2024 Cyber-Physical Systems (CPS) Rising Stars Workshop, which is sponsored by the U.S. National Science Foundation and ACM SIGBED, will be held at the University of Virginia (Charlottesville, VA) on May 29, 2024.
Abstract
One of the significant challenges confronting our society today is ensuring the health and productivity of dairy herds which are vital to our food supply. Diseases such as mastitis and lameness pose a continuous threat to the economic viability of dairy farms. In response to these challenges, our project aims to harness the potential of modern Cyber-Physical Systems (CPS), advanced data analytics, AI/Machine Learning technologies to deliver a comprehensive solution for disease control in dairy farming. We aim to develop an innovative, integrated system to manage and improve dairy herd health effectively and sustainably. Our approach leverages state-of-the-art biosensors for real-time disease detection and a unique social network model that monitors animal-environment and animal-animal interactions. We also develop a digital twin platform for the farm to answer what-if questions for disease control decisions. This not only improves our understanding of disease transmission but also empowers dairy farm personnel with timely information for optimized herd management. This project is realized in collaboration with a dairy farm in the state of Tennessee and is anticipated to have nationwide implications.The proposed research engages in several critical areas of data analytics, disease control, and farm management, such as: (1) the development and optimization of innovative biosensors for real-time disease detection, (2) the creation of a farm-wide, real-time cow-based social network to track the mobility patterns of cows and their interactions, (3) the application of AI and big-data analytics to process, interpret and learn from the extensive data generated, and (4) the implementation and validation of the proposed system on real-world dairy farms, facilitating the transition from research to practice. Our close collaboration with farm management facilitates the broader impacts of the proposed work. Our findings will be disseminated across various platforms, targeting the scientific community, precision agriculture researchers, and the general public. Our educational initiatives and community engagement efforts will target the new generation of scientists and researchers, particularly encouraging participation from underrepresented groups, including African-Americans, Native Americans, Hispanics, and female students. The outcomes of our research, as well as all relevant educational resources produced during this project, will be accessible to the public via our project's online website.
Charles Qing Cao
Performance Period: 07/01/2021 - 06/30/2026
Institution: University of Tennessee Knoxville
Sponsor: USDA
Award Number: 2149788
Abstract
Heat stress costs the US dairy industry an estimated $1.5 billion annually due to decreases in milk production and reproductive efficiency and, in periods of extreme heat stress, an increase in fatalities. Heat stress can also threaten animal welfare, putting at risk the long-term social acceptability of dairy farming. Despite the efficiencies that accompany high milk yield, high-producing cows in particular present challenges for the dairy farmer in terms of their vulnerability to heat stress. Heat stress is expected to remain a critical issue for the dairy industry in the coming decades as climate change models predict further increases in average temperatures and the frequency of heat waves. Unfortunately, in many of the currently operating dairy barns, cooling systems are designed and controlled without proper sensing and decision-making capabilities, and thus often fail to deliver effective cooling.This project seeks to solve this inefficiency and address the imminent challenge by developing a control system that measures and predicts the level of heat stress and maintains an optimal microclimate inside a dairy barn to minimize the impact of heat stress. The developed system will largely automate the decisions typically associated with heat-stress management and provide farmers with actionable information while relieving them of the dilemmas created by a surfeit of often contradictory options. More specifically, we propose to combat heat stress in dairy cattle by using a novel engineering system that relies on continuous physiological and micro-environmental sensing, real-time thermal-induced behavior analysis, and computational fluid dynamics (CFD)-based microclimate control. We will develop sensors that can continuously monitor cows' physiological and behavioral responses in real time. The physiological and behavioral data, augmented by real-time micro-environmental measurement, will be used to measure the overall level of heat stress in real time. A real-time analysis of the heat-stress inducing factors will then be used to adjust the barn's cooling system and prevent the animals' heat-induced stress from worsening. To achieve effective cooling with a minimum amount of energy and water consumption, CFD-based analysis and optimization will be used to find the optimal design and most efficient runtime control. A prototype will be implemented and deployed in UW-Madison dairy barns with different housing and ventilation systems in order to evaluate performance and cost effectiveness.The proposed research is expected to initiate a new direction in the drive to develop CPS for closed-loop control of dairy barns, one that should also advance the use of real-time data analytics and control in other precision-agriculture domains. This research will greatly improve modern dairy barns' ability to cope with heat stress while also reducing energy and water resource consumption and enhancing the welfare of dairy cattle. Moreover, the proposed research should prove highly relevant to other livestock industries that must deal with the effects of heat stress in indoor-housed animals (e.g., poultry, swine). The diversity of the research team we have created and the highly interdisciplinary nature of the research will involve a broad range of underrepresented students in computing and engineering, and by engaging the wider community through UW-Madison Extension programs and various outreach programs, we will be able to raise public awareness of sustainable farming and animal welfare.
Younghyun Kim
Performance Period: 07/01/2021 - 06/30/2024
Institution: University of Wisconsin - Madison
Sponsor: USDA
Award Number: 2038452
Abstract
The conventional management approach in agricultural production is applying water and chemicals at a fixed rate throughout the whole field. This practice did not consider the existence of in-field variability in spatial and temporal domains and results in over- and under-applications on crop and serious environment issues such as nitrogen leaking to and contamination of groundwater. Variable-rate technology is the key technology in agricultural production to address the in-field variability, maximize yield and profit, and minimize the agricultural inputs or footprints on the environment. Hence, this project pulls a multidisciplinary effort to address challenges in today's variable-rate technology (VRT) in agricultural production by tightly integrating sensing, networking, AI-based and process-based data analytics, and control systems with classic plant and soil biophysical principles and well-recognized management practices, to provide a generalizable and scalable framework for the real-time in-season variable-rate application. Meanwhile, it also improves the data analytics and decision-making models by turning the massive amount of data generated in daily agricultural production into a dynamic and distributed training process for model self-improving while keeping the farmers' privacy and computational efficiency.
Yeyin Shi
Dr. Yeyin Shi is currently an associate professor of agricultural intelligence in the Department of Biological Systems Engineering at the University of Nebraska-Lincoln. Her research and teaching program aims to use advanced sensing and information technology as well as data analytics to automate and improve the decision making and application processes in agricultural production and natural resource management.
A major platform that Dr. Shi has been extensively working on is the unmanned aircraft systems (or drones). She and her group have developed data analytics with state-of-the-art computer vision and machine and deep learning techniques to monitor vegetation growth, biomass, and abiotic and biotic stresses, or predict yield. They also go beyond sensing but incorporate sensing and actuation for intelligent aerial applications. Dr. Shi’s research has been mainly funded by federal and state agencies.
Dr. Shi is also passionate about college education and has been part of the thrust in the development of precision and digital agriculture curriculums. She teaches Site-Specific Crop Management (Precision Agriculture), Technologies and Techniques in Digital Agriculture, and Aerial Imagery Processing and Analysis to undergraduate and graduate students.
Dr. Shi received her Ph.D. and M.S. degrees both from Oklahoma State University and worked as postdoctoral researchers at the Texas A&M University and the University of Florida prior to join in UNL.
Performance Period: 04/01/2021 - 05/31/2024
Institution: University of Nebraska-Lincoln
Sponsor: USDA
Award Number: 2039055
Abstract
Frost events present a significant threat to perennial crop producers due both to potential bud damage and mitigation costs. Growers struggle to optimize the utilization of three primary active mitigation methods--heaters, wind machines, and over- and under-tree sprinkler irrigation--due to a reliance on regional weather forecasts, rules-of-thumb, and heuristic decision-making. Our overall project objective is to develop an intelligent frost mitigation control actuation system, based on micrometeorological monitoring and site-specific, mitigation-informed weather and bud temperature forecasts, that reduces both crop damage and mitigation costs. Washington State is the top producer of sweet cherries and blueberries--both vulnerable to frost damage and the focus of this project.Toward the overall project objective, we will pursue three specific aims: Aim #1. Integrate surface?and?aerial?meteorological?observations into?field-specific,?short-term?forecasts?of weather variables relevant to frost mitigation. Aim #2. Develop localized weather data-driven intelligent crop loss management system through real-time actuation of either-or combinations of active frost mitigation techniques, i.e. wind machines (frost fans), over-tree and under-tress fixed spray systems. Aim #3. Assess grower?evaluation/validation of decision aid tools and prototype performance.
Dave Brown
Performance Period: 02/15/2021 - 02/14/2025
Institution: Washington State University
Sponsor: USDA
Award Number: 2039046
Abstract
Frost events present a significant threat to perennial crop producers due both to potential bud damage and mitigation costs. Growers struggle to optimize the utilization of three primary active mitigation methods--heaters, wind machines, and over- and under-tree sprinkler irrigation--due to a reliance on regional weather forecasts, rules-of-thumb, and heuristic decision-making. Our overall project objective is to develop an intelligent frost mitigation control actuation system, based on micrometeorological monitoring and site-specific, mitigation-informed weather and bud temperature forecasts, that reduces both crop damage and mitigation costs. Washington State is the top producer of sweet cherries and blueberries--both vulnerable to frost damage and the focus of this project.Toward the overall project objective, we will pursue three specific aims: Aim #1. Integrate surface?and?aerial?meteorological?observations into?field-specific,?short-term?forecasts?of weather variables relevant to frost mitigation. Aim #2. Develop localized weather data-driven intelligent crop loss management system through real-time actuation of either-or combinations of active frost mitigation techniques, i.e. wind machines (frost fans), over-tree and under-tress fixed spray systems. Aim #3. Assess grower?evaluation/validation of decision aid tools and prototype performance.
Dave Brown
Performance Period: 02/15/2021 - 02/14/2025
Institution: Washington State University
Sponsor: USDA
Award Number: 2039046
Abstract
Cyber-physical systems (CPS) have now started to play an increasingly important role in autonomous sensing, analysis, and tasking in a variety of agricultural settings ranging from sustainable farming to livestock monitoring. Many of these settings demand real-time analytics, at varying timescales, and the CPS devices have to coordinate among themselves over a variety of wireless networks. As various actors in these settings---from farmers to big agro companies---have much to gain from manipulating the results of these distributed systems, it is important to make these systems fault-tolerant and secure. This project, COPIA, seeks to provide the fundamental secure distributed computing primitives tailored for real-time agro-analytics in the face of malicious faults and network failures. Despite more than four decades of work on secure distributed computing, this CPS domain introduces new requirements that COPIA will address through fundamental innovations. First, COPIA will incorporate a principled framework for comparing energy costs of protocols and deriving optimal choices of cryptographic primitives to optimize energy use. This framework will permit leveraging CPS-specific opportunities, e.g., the difficulty for an adversary to equivocate (or offer two conflicting statements to two different neighbours) due to the omnidirectional nature of wireless links. Second, COPIA will achieve consensus in dynamic networks, i.e., where CPS nodes are mobile (e.g., drones). The technical challenge here is that the communication graph of nodes dynamically changes; most existing work assumes graph connectivity is unchanging throughout the execution of the protocol. Third, COPIA will address privacy in these distributed computing protocols, as the farmers are increasingly worried about companies extracting trade secrets from sensor data. This thrust involves hardening distributed computing protocols so that a limited number of node compromises does not divulge secrets. Overall, COPIA will make vital steps toward building novel, secure distributed CPS solutions for real-time analytics by addressing significant sources of safety, privacy, and availability vulnerabilities with the current CPS solutions. The project formulates an integrated research agenda that couples a strong theoretical component with an ambitious systems research component. As the importance of precision agriculture and the associated cybersecurity threat and potential vulnerabilities grow, the proposed principled approach will become a necessity for secure real-time agro-analytics.The team will demonstrate the innovations on experimental farms at Purdue University, secure embedded testbeds consisting of heterogenous embedded nodes at lab-scale, and on data from commercial livestock IoT monitoring deployments. Through these demonstrations, COPIA will energize a student community working on security of distributed embedded systems, and a community of farmers who realize profitability and environmental sustainability, e.g., reduced fertilizer use, early detection of livestock anomalies, and improved reliability and security of their monitoring systems.
Michael Reiter
Performance Period: 02/01/2021 - 01/31/2025
Institution: Duke University
Sponsor: USDA
Award Number: 2038566
Abstract
Cyber-physical systems (CPS) have now started to play an increasingly important role in autonomous sensing, analysis, and tasking in a variety of agricultural settings ranging from sustainable farming to livestock monitoring. Many of these settings demand real-time analytics, at varying timescales, and the CPS devices have to coordinate among themselves over a variety of wireless networks. As various actors in these settings---from farmers to big agro companies---have much to gain from manipulating the results of these distributed systems, it is important to make these systems fault-tolerant and secure. This project, COPIA, seeks to provide the fundamental secure distributed computing primitives tailored for real-time agro-analytics in the face of malicious faults and network failures. Despite more than four decades of work on secure distributed computing, this CPS domain introduces new requirements that COPIA will address through fundamental innovations. First, COPIA will incorporate a principled framework for comparing energy costs of protocols and deriving optimal choices of cryptographic primitives to optimize energy use. This framework will permit leveraging CPS-specific opportunities, e.g., the difficulty for an adversary to equivocate (or offer two conflicting statements to two different neighbours) due to the omnidirectional nature of wireless links. Second, COPIA will achieve consensus in dynamic networks, i.e., where CPS nodes are mobile (e.g., drones). The technical challenge here is that the communication graph of nodes dynamically changes; most existing work assumes graph connectivity is unchanging throughout the execution of the protocol. Third, COPIA will address privacy in these distributed computing protocols, as the farmers are increasingly worried about companies extracting trade secrets from sensor data. This thrust involves hardening distributed computing protocols so that a limited number of node compromises does not divulge secrets. Overall, COPIA will make vital steps toward building novel, secure distributed CPS solutions for real-time analytics by addressing significant sources of safety, privacy, and availability vulnerabilities with the current CPS solutions. The project formulates an integrated research agenda that couples a strong theoretical component with an ambitious systems research component. As the importance of precision agriculture and the associated cybersecurity threat and potential vulnerabilities grow, the proposed principled approach will become a necessity for secure real-time agro-analytics.The team will demonstrate the innovations on experimental farms at Purdue University, secure embedded testbeds consisting of heterogenous embedded nodes at lab-scale, and on data from commercial livestock IoT monitoring deployments. Through these demonstrations, COPIA will energize a student community working on security of distributed embedded systems, and a community of farmers who realize profitability and environmental sustainability, e.g., reduced fertilizer use, early detection of livestock anomalies, and improved reliability and security of their monitoring systems.
Aniket Kate
Performance Period: 02/15/2021 - 02/14/2024
Institution: Purdue University
Sponsor: USDA
Award Number: 2038986
Abstract
Plant diseases pose an increasing threat to the nation's food supply and biosecurity in a changing environment. There is a growing risk of plant pathogens spreading through shipments of stock plants between production facilities, and from production facilities to growers or to retailers and consumers. Recent failures to prevent plant disease emergence and spread in the US has resulted in major economic losses (20-40% of total yield) for growers. One central challenge to preventing accidental pathogen dissemination and disease outbreaks is that many plant diseases are difficult to detect at an early stage and infected (possibly asymptomatic) plants can spread pathogens undetected when being shipped from one location to another. Once an emerging disease takes foothold in a new environment, eradicating such disease is extremely challenging.As our model system, we focus on disease detection and control in transplant facilities, which produce seedlings that will later be planted in production fields. These facilities are an amplifier of diseases in the agriculture production chain because of the use of greenhouses with limited environmental control and high plant density (average 800 plants per square meter, growing on plastic trays), that facilitate disease spread. A typical transplant facility grows over 500,000 plants at a time; therefore, manual scouting of the facility is time consuming and ineffective. Poor training in plant pathology and tight schedules of employee limit sensitivity of disease detection and spatial-temporal resolution. In summary, the transplant industry is a major source of disease outbreaks and can thus be a key point for disease control in the agriculture supply chain. To reduce the contribution of transplant facilities to disease outbreaks, we plan to develop a novel disease detection and control CPS to provide closed-loop disease control at a very early stage.This project includes three main objectives. First, an open-source robotic gantry system will be developed to automate the process of greenhouse scouting, plant sampling and plant removal to prevent disease spread. Second, a microfluidic device will be implemented to automate the process of library preparation from bacterial DNA extraction, amplification to metagenomic sequencing. Third, controlled experiments will be performed to determine the best methods for disease detection via nanopore, meta-genomic sequencing, machine learning, and computer vision. Different control strategies will be tested to determine the best approach that integrates data from metagenomic sequencing and the analysis of images of infected plants.The immediate goal of our project is to reduce the spread of plant diseases in the agriculture production chain by focusing on reducing disease instances in the transplant facilities and greenhouses. Our approach is to reduce the loss of plant products by optimization of disease detection and control strategies through robotics and automated genetic sequencing. In the long run, our project will contribute to the improvement of automation and reduction of the labor cost in agriculture. Our system, if successful, will reduce the loss of plants due to diseases for greenhouse operations and in plant production systems that utilizes transplant facilities.
Song Li
Performance Period: 01/15/2021 - 01/14/2025
Institution: Virginia Polytechnic Institute and State University
Sponsor: USDA
Award Number: 2038676
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