Source: VIRGINIA POLYTECHNIC INSTITUTE submitted to NRP
COLLABORATIVE RESEARCH: CPS: MEDIUM: GREENER PASTURES: A PASTURE SANITATION CYBER PHYSICAL SYSTEM FOR ENVIRONMENTAL ENHANCEMENT AND ANIMAL MONITORING
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
ACTIVE
Funding Source
AFRI COMPETITIVE GRANT
Reporting Frequency
Annual
Accession No.
1026249
Grant No.
2021-67021-34769
Cumulative Award Amt.
$539,978.00
Proposal No.
2020-11362
Multistate No.
(N/A)
Project Start Date
Jul 1, 2021
Project End Date
Jun 30, 2025
Grant Year
2021
Program Code
[A7302]- Cyber-Physical Systems
Recipient Organization
VIRGINIA POLYTECHNIC INSTITUTE
(N/A)
BLACKSBURG,VA 24061
Performing Department
Animal and Poultry Sciences
Non Technical Summary
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.
Animal Health Component
50%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
0%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
1020110107010%
1020120107010%
1020210202010%
1110320310010%
3070780107020%
4020120208020%
4020120202020%
Knowledge Area
102 - Soil, Plant, Water, Nutrient Relationships; 111 - Conservation and Efficient Use of Water; 307 - Animal Management Systems; 402 - Engineering Systems and Equipment;

Subject Of Investigation
0120 - Land; 0320 - Watersheds; 0780 - Grasslands, other; 0110 - Soil; 0210 - Water resources;

Field Of Science
2020 - Engineering; 1070 - Ecology; 2080 - Mathematics and computer sciences; 3100 - Management;
Goals / Objectives
1. Characterize grazing animal behavior int he presence of an autonomous robot within the pasture.2. Determine mechanical specifications and procedures for an autonomous robot to perform manure management tasks.3. Verify system performance within empty pastures and assess system performance within populated pastures.
Project Methods
Sub Aim 1.1. Develop baseline characterization of animal movements, manure deposition, and associated soil moisture and nutrient profiles without a robot present. We will house 40 cattle and 10 horses in the SmartFarm Innovation Testbed sites at the Middleburg AREC and the Shenandoah Valley AREC. These sites are equipped with surveillance cameras that continuously monitor animal behaviors. Observations will be collected for 15-d and insight gleaned from observations will be tested for consistency over the subsequent 15-d. To account for seasonal variation, this exercise will be performed 4 times during the 1st year of the project, once per season. During each 30-d period, animals will wear a sensor strapped around the tail head.During the grazing periods, we will measure the spatiotemporal dynamics of soil moisture and nutrient content using a handheld soil moisture probe and the LaMotte soil test kit (LaMotte STH5 Combination soil outfit; LaMotte Co., Chestertown, MD to measure N, P, and organic matter from grid samples collected at 10m resolution throughout the testbed. Moisture data will be used to understand the spatial and temporal nature of hydrologically sensitive areas throughout the field. Soil nutrient data will be used to develop a spatial map of the soil N and P content. Together the soil nutrient and soil moisture data provide an indication of where nutrient hotspots occur (e.g., areas where hydrologic activity and high nutrient availability coincide).Sub Aim 1.2. Evaluate robot-livestock interactions to determine flight zones, time to habituation, effective habituation strategies, and management to minimize destructive behaviors.An existing autonomous snowplow robot will be configured with sensors for Sub Aims 1.2, 1.3, 2.1, and 2.2. The plow will be removed. The robot will be controlled remotely or follow a predefined path.We will evaluate robot-livestock interactions using 3 separate behavioral tests for animals that have not been habituated to the robot: stationary robot, moving robot, and flight distance test60. These tests will allow us to detect animal's sensitivity to the robot, effects of the robot on measures of stress and welfare (heart rate variability, rumination61) and rates of any destructive behaviors (charging, head butting, kicking, striking) or destructive precursors (including head lowering, nostril blowing)62 that might result in robot destruction or indicate reduced livestock welfare. For any agnostic behavioral event we detect, we will also determine the speed, angle of approach as it pertains to the animal, and proximity of the robot 10-s before the first agonistic behavior we can detect. Using a remote-controlled robot, we will evaluate livestock behavior towards a robot over four hours each in stationary and moving robot tests, and as a function of robot speed and proximity. We will evaluate flight distance as a function of robot speed, direction as it pertains to the animal, and proximity. Our time-series data will allow us to identify changes potentially indicative of habituation60,63, as well as parameters (speed, proximity to animals) of the robot that might increase destructive behaviors that could damage the robot or are indicative of decreased welfare. We will use the data from these behavioral tests to program livestock-robot interactions, including how close the robot can approach the livestock and how quickly, and how the angle of approach might affect those measures.Sub Aim 1.3. Evaluate animal movements, soil characteristics, and manure deposition when co-housed with an autonomous robot.The impact of co-housing a robot with the animals will be tested with the same experiential design (soil testing, animal wearable sensors, 2 grazing periods, 4x/year) described in Sub Aim 1.1; however, the robot will be programmed to drive a random path through the field for 4 hours, once a day to simulate manure management during the second 15-d period.Sub Aim 2.1. Capture of pasture physical characteristics and conditions. Spatial distributions of soil water, N, P, and organic matter contents are expected to change throughout the year and in response to animal defecation patterns. HB100 10Ghz RADAR sensors have demonstrated the ability to characterize soil moisture66.Data will be collected using the PSR1 in each pasture in a grid pattern (where feasible) using both IOSs and SMs immediately following the measurements collected in Sub Aim 1.1. The study of the nutrient flow within the pasture will inform the development of robot planning algorithms that may more effectively and efficiently mitigate poor flow conditions. The robot will also be configured to acquire field characteristic data using GPS, precision altimeter, 3.3-10 GHz (US/FCC models) three-dimensional radio-frequency based sensor, an array of linearly polarized broadband radar, 10GHz radar, visible and near-infrared multispectral sensors. Soil saturation patterns will be collected to determine areas of soil sensitivity and identify areas too saturated for robot movement.Sub Aim 2.2. Create robot navigation and task performance methods, procedures, and algorithms to achieve the elements of pasture maintenance. Because it can be very difficult and time-consuming to predict all possible situations that the robot may encounter, we will leverage recent advances in deep reinforcement learning that have shown the ability to address driving skill in unstructured environments77,78. Virtual environments replicating terrain measured in the pastures will be created. A simulated robot will navigate these environments with all sensor data collected. When the platform has a prohibited event (flips over or loses steering control and collides with an obstacle), data from before the event is tagged to identify it as indicating imminent failure. The data will be used to train a network for dynamic platform stability.Sub Aim 2.3. Develop robot control algorithms for the tilling and loading/moving manure. Simulation will be used to develop controllers for robots. Algorithmic and deep neural network approaches will be used to generate control systems resilient to operation in a pasture and real-time learning based on the expected versus sensed effects of control plans will be used to continually improve and/or adapt the actuation to the specific situation. Sub Aim 3.1. Model nutrient flows within pasture systems to identify best-practice reallocation strategies for manure. Data collected in aims 1.1 and 2.1 will be used to develop a livestock-environmental model41,42,85. This model will serve several purposes; 1) it determine the initial hydrological sensitivity map of pasture areas, based on terrain and soil characteristics, which will be used by PSR2 to learn which areas of the pasture are typically nutrient source areas, and 2) it will be used in a forecasting mode to predict soil nutrient and soil moisture levels40,86. Sub Aim 3.2. Implement physical robot consistent with measurements of Aims 1 & 2. As described in Aim 1, our existing mobile robot will be used to facilitate the majority of Aims 1 & 2 which will yield data critical for use in the design of robots. The robots will be developed using existing farm equipment as the base hardware. The process will begin with a Gazebo79 simulation for early testing, a benefit of using the robot operating system (ROS)/Gazebo framework.

Progress 07/01/23 to 06/30/24

Outputs
Target Audience:The target audience during this reporting period included the scientific community and the general public. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The students funded by this project have been afforded traning and professional development activities including traveling to conferences to present on their research and attend organized, related professional development opportunities. This cycle, we also provided numerous workshops on sensor design and consutruction to the broader scientific community How have the results been disseminated to communities of interest?Results have been disseminated to the communities of interest through conference presentations and seminars given at neighboring and partner institutions, as well as through peer-reviewed publications extending to the scientific community. What do you plan to do during the next reporting period to accomplish the goals?During the next reporting period, we will complete final testing of the pasture sanitation system in a field setting.

Impacts
What was accomplished under these goals? Toward Objective 1. Characterize grazing animal behavior in the presence of an autonomous robot within the pasture. - We assessed whether pastured animal heart rate monitoring could be used to more precisely monitor animal responses to autonomous robots win the pasture environment. These data have been collected and analysis is ongoing. Toward Objective 2. Determine mechanical specifications and procedures for an autonomous robot to perform manure management tasks. - We designed and tested two prototype manure collection devices for their effectivness in collecting real cattle or horse manure from pasture environments. The mass of manure removed, the nutrient composition of the residual manure were measured as primary indicators of protoype effecacy. These data have been collected and analysis is ongoing. Toward Objective 3. Verify system performance within empty pastures and assess system performance within populated pastures. - Our team continued to refine and test the Robot Data to Archive and Model to Robot data workflow via MQTT brokering. Two-way interactive data transport for robot target acquisition and lead has been tested and we are working to optimize this workflow. The final step in this process will be to evaluate the full system in field in Spring of 2025.

Publications

  • Type: Journal Articles Status: Under Review Year Published: 2024 Citation: Chen, C. P., and Robin White. "Common Pitfalls in Evaluating Model Performance and Strategies for Avoidance." Robin, Common Pitfalls in Evaluating Model Performance and Strategies for Avoidance (2024).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Wright, Ryan, Charleez Simcik, and Robin R. White. "PSXI-8 Comparison of Spectral and Motion Sensing for Grazing Behavior Determination in Extensive Systems." Journal of Animal Science 101.Supplement_3 (2023): 596-597.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Wright, Ryan, Alyssa Ganino, and Robin R. White. "PSXI-7 Sensing of Defecation and Urination Events Through Carbon Dioxide Monitoring." Journal of Animal Science 101.Supplement_3 (2023): 599-599.
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Naughton, Samantha G., et al. "Photoplethysmography pulse sensors designed to detect human heart rates are ineffective at measuring horse heart rates." (2023): 20230462974.
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Garna, Roja K., et al. "Employing higher density lower reliability weather data from the Global Historical Climatology Network monitors to generate serially complete weather data for watershed modelling." Hydrological Processes 37.11 (2023): e15013.


Progress 07/01/22 to 06/30/23

Outputs
Target Audience:The target audience during this reporting period included the scientific community and the general public. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The students funded by this project have been afforded traning and professional development activities including traveling to conferences to present on their research and attend organized, related professional development opportunities. This cycle, we also provided numerous workshops on sensor design and consutruction to the broader scientific community. How have the results been disseminated to communities of interest?Results have been disseminated to the communities of interest through conference presentations and seminars given at neighboring and partner institutions, as well as through peer-reviewed publications extending to the scientific community. What do you plan to do during the next reporting period to accomplish the goals?Over the next reporting period, we will take our preliminary findings on animal behavior, soil characteristics, and manure deposition patternswithin the test fields to build model-based representations of the pasture environment to inform on manure handing objectives. We will expand our testing ofanimal/robot interactions and habituation strategies to use the full scale PSR2, rather than the test version used to date. We will work with the team at CWR to continue to provide field-testing opportunities for the robot, and to refine the manure pickup, storage, and deposition mechanisms.

Impacts
What was accomplished under these goals? Toward Objective 1. Characterize grazing animal behavior in the presence of an autonomous robot within the pasture. - Assessed two pastures of cattle and four pastures of horses in a standardized test (stationary/moving/flight test). This paper is ins prep and should be submitted within a few months - Assessed two pastures of cattle and four pastures of horses in a habituation test (how long until all animals resume normal activities) - Assessed two pastures of cattle and four pastures of horses in a standardized test with an autonomous (versus human-controlled) robot in a habituation test Toward Objective 2. Determine mechanical specifications and procedures for an autonomous robot to perform manure management tasks. We designed and tested a manure analog to enable our engineering colleagues at Case Western to explore manure pickup devices for the robot. 3. Verify system performance within empty pastures and assess system performance within populated pastures. Development and testing of the Robot Data to Archive and Model to Robot data workflow via MQTT brokering. Specifically, we have been testing the pre-processing of the novel, inexpensive soil sensing radar unit mounted onto the robot with optimal signal isolation, alignment with geolocation and orientation data from other robot sensors, and packaging for transport to Virginia Tech Cloud Data Stores. Two-way interactive data transport for robot target acquisition and lead has been started with the first testing fall of 2023. Aligning robot-mounted soil sensing radar unit with TDR geolocation measurements to determine if there is a relationship between the two.

Publications

  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Adams, I., Quinn, R., Lee, G., Kroeger, A., & Feuerbacher, E. N. (2023, July). Autonomous versus manual control of a pasture sanitation robot. In Conference on Biomimetic and Biohybrid Systems (pp. 363-377). Cham: Springer Nature Switzerland.
  • Type: Other Status: Published Year Published: 2023 Citation: Workshop: Building Upon the IoT Projects within the EarthCube Community, demonstrating the VT, USDA, SparkFun collaborative rapidly deployable watershed sensing board featuring 15-minute setup and networking of Temp, Pressure, RH, VOC, 9DF Motion, Weighing Lysimeter, Solar Powered Cellular Base Station. EC2023: Building Upon the EarthCube Community, Information Sciences Institute, Marina Del Rey (Los Angeles), June 27-28, 2023 https://isi-usc-edu.github.io/building-upon-the-earthcube-community/program-Dan
  • Type: Other Status: Published Year Published: 2023 Citation: Workshop: 2023 ESIP Sensing to Archive, an Applied IoT Workshop for the ESIP Community to be introduced and explore Sensing to DAC data workflows; based on the VT, USDA, SparkFun collaborative rapidly deployable watershed sensing board. July 2023 ESIP Meeting, Burlington, VT. July 18-21, 2023, https://2023julyesipmeeting.sched.com/event/1NocI/wireless-environmental-monitoring-community-of-practice-session-hosted-by-the-envirosensing-cluster-Dan
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Enhanced Identification of the Spatiotemporal Dynamics of Variable Source Area Contributors: Binyam Workeye Asfaw, Sabrina Mehzabin, Daniel Fuka, Ian Adams, Robin White, Greg Lee , Zachary M. Easton. 2023. CAIA big event CALS VT Poster presentation
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Reis BR, Nguyen T, Sujani S, White RR. Open-Source Wearable Sensors for Behavioral Analysis of Sheep Undergoing Heat Stress. Applied Sciences. 2023 Aug 16;13(16):9281.
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Kaur U, Malacco VM, Bai H, Price TP, Datta A, Xin L, Sen S, Nawrocki RA, Chiu G, Sundaram S, Min BC. Invited Review: Integration of Technologies and Systems for Precision Animal AgricultureA Case Study on Precision Dairy Farming. Journal of Animal Science. 2023 Jun 19:skad206.
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Naughton SG, Gleason CB, Leeth CM, White RR. Brief research report: Photoplethysmography pulse sensors designed to detect human heart rates are ineffective at measuring horse heart rates. Frontiers in Animal Science. 2023 Mar 21;4:1103812.


Progress 07/01/21 to 06/30/22

Outputs
Target Audience:The target audience during this reporting period included the scientific community and the general public. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The students funded by this project have been afforded traning and professional development activities including traveling to conferences to present on their research and attend organized, related professional development opportunities. How have the results been disseminated to communities of interest?Results have been disseminated to the communities of interest through conference presentations and seminars given at neighboring and partner institutions, as well as through peer-reviewed publications extending to the scientific community. What do you plan to do during the next reporting period to accomplish the goals?Over the next reporting period, we will take our preliminary findings on animal manure deposition and spatial heterogeneity within the test fields to build model-based representations of the pasture environment to inform on manure handing objectives. We will continue to explore animal/robot interactions, and test various habituation strategies, and will continue to refine animal behavior and defecation sensing methods. We will work with the team at CWR to continue to provide field-testing opportunities for the robot, and to refine the manure pickup, storage, and deposition mechanisms.

Impacts
What was accomplished under these goals? Under Goal 1:In conjunction with the robot team from Case Western Reserve, we have deployed a currently working robot as a proxy for the final robot in this project. In two pastures of cattle and four pasture of horses, we have assessed the animals' responses to a moving robot, stationary robot, and to a robot moving directly at them (flight distance test) as a basic measure of responding to a robot. Each pasture was tested three times. From these tests, we have been able to determine flight distance, number of animals approaching or retreating from the robot, and stress behaviors, as well as how this changed across three days of testing. Ian Adams has written a paper on this work and we are working on a follow-up paper including different measures of animal behavior. In a subsequent deployment of the robot, we again assessed two pastures of cattle and three pastures of horses to the latency until all animals in the herd resumed non-vigilant behavior across three days of testing for each pasture. This will allow us to determine the degree of disruption an autonomous vehicle could have on animal behavior and welfare. Under Goal 2: This goal is under development by our collaborators at Case Western Reserve. Under Goal 3: In conjunction with the robot team from Case Western Reserve, we have deployed a test platform within empty pastures on 3 seperate site visits. Those pastures have been explored in grid search patterns, through path-based navigation, and through targeted obstacles. Lidar, radar, and spectal data have been collected on these runs, and will be used over the next two years to inform more targeted updates in robot design and navigation approaches.

Publications

  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Deval, C., E.S. Brooks, M. Dobre, R. Lew, P.R. Robichaud, A. Fowler, J. Boll, A.S. Collick, Z.M. Easton. 2022. Pi-VAT: A web-based visualization tool for decision support using spatially complex water quality model outputs. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2022.127529
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Fleming, P., K.S. Stephenson, A.S. Collick, Z.M. Easton. 2022. Targeting for Nonpoint Source Pollution Reduction: A Synthesis of Lessons Learned, Remaining Challenges, and Emerging Opportunities. Journal of Environmental Management. DOI: 10.1016/j.jenvman.2022.114649
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Modi, P., J. Czuba, Z.M. Easton. 2022. Coupling a land surface model with a hydrodynamic model for regional flood risk assessment due to climate change: application to the Susquehanna River. Journal of Flood Risk Management. http://doi.org/10.1111/jfr3.12763
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Fuka, D., White, R., Garna, R., Stamps, D., Buelle, E., Collick, A., & Easton, Z. 2021. Seamless Machine Learning Architecture Access to Long-Tail In-Situ Data through EarthCube's BALTO Cyberinfrastructure. In AGU Fall Meeting 2021
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Fuka, D., White, R., Roqueto dos Reis, B., Kaveh Garna, R., Buell, E., Collick, A, Easton, Z. 2021. Ultra-Low-Power LoRa-Embedded Microcontrollers Simplifying Rapidly Deployable Inexpensive Weather, Soil, and Streamflow Sensor Designs. In AGU Fall Meeting 2021.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Kaveh Garna, R., Fuka, D., White, R., Faulkner, J., Buell, E., Roqueto dos Reis, B. Easton, Z. 2021. Watershed Model Parameter Estimation in Low Data Environments. In AGU Fall Meeting 2021.