Source: VIRGINIA POLYTECHNIC INSTITUTE submitted to
CPS: MEDIUM: COLLABORATIVE RESEARCH: CLOSED LOOP SUSTAINABLE PRECISION ANIMAL AGRICULTURE
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
EXTENDED
Funding Source
Reporting Frequency
Annual
Accession No.
1017782
Grant No.
2018-67007-28452
Project No.
VA-RW003
Proposal No.
2018-02492
Multistate No.
(N/A)
Program Code
A7302
Project Start Date
Jul 15, 2018
Project End Date
Jul 14, 2022
Grant Year
2018
Project Director
White, R.
Recipient Organization
VIRGINIA POLYTECHNIC INSTITUTE
(N/A)
BLACKSBURG,VA 24061
Performing Department
Animal and Poultry Science
Non Technical Summary
Precision field crop agriculture is seen as one of a handful of key, early-adoption, commercial opportunities for drones and drone-based technologies because of the demonstrated ability to survey large areas from an elevated vantage point to measure and map crop needs. The time scales for these types of interventions, while highly profitable, are often measured in days or weeks. Precision animal agriculture (PAA), on the other hand, presents a much richer environment for cyber-physical systems (CPS) research. Animals, individually, are complex organisms that require constant nutritional adjustment, yet they are social beasts with herd behavior that emerges from the collective. For issues of nutrition, health, productivity and efficiency, animal agriculture must treat both the individuals and the collective, making it ideal for the science and application of CPS principles. With growing global awareness of the negative effects of livestock production on non-renewable and renewable resources, concurrent with the negative effects of global population growth and the need to feed more mouths, the transformational impact of CPS on the largely unexplored realm of precision animal agriculture is enormous. This is particularly critical as a significant portion of the projected increases in global food production is anticipated to come from ruminants. This proposal presents basic science exploring the complex relationship between individual animal and herd behaviors on agriculture systems efficiency, while demonstrating its potential on the specific area of dairy farm management with the goal of improving sustainability and efficiency. The long-term objective of this team's research is to develop the foundations for CPS that apply, generically, to intensified management of the individual animal and herd in areas of nutrition, health, productivity, and efficiency that underpin this new area of precision animal agriculture.
Animal Health Component
10%
Research Effort Categories
Basic
75%
Applied
25%
Developmental
0%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
3073410101025%
3073410106025%
3073410202050%
Goals / Objectives
A set of 4 specific goals have been identified for this project:Develop the decision layer of the generic precision animal agriculture CPS that incorporates flexible animal and herd models, guided by real data examples. Utilize existing data to test model parameterization strategies for feeding and health to reformulate supplement mixes based on real-time performance inputs representing both the individuals and the herd.Develop the network layer of the generic precision animal agriculture CPS, including body networking to extract embedded sensor data from individual animals; herd networking and tracking to consolidate and report individual and collective herd data with appropriate edge analytic capabilities; cloud computing capacity and software algorithms to receive, log, and interpret data from the edge to the core; and on-farm production interfaces to evaluate the production environment of the entire system.Develop the physical layer artifacts for generic animal and herd networking as well as the purpose-built sensors and actuators for the dairy farm example CPS. Generic elements include an enhanced animal collar/mobility sensor and field-ready wireless access points. The purpose-built elements include an in-dwelling rumen sensor (developed separately, but this project is not dependent on its availability), automated feed delivery device, cow weight sensor, and in-line milk analysis equipment.Develop new knowledge based on data networks that link animal and herd data with increased efficiency, profitability and animal well-being. Link the physical, network, and decision layers of the CPS and deploy, test and validate on-farm in two separate, networked installations.
Project Methods
Goal 1.1: (White and Daniels) Develop a profile of naturally occurring fluctuations in efficiency of production for cattle using remotely sensed behavioral and physiological observations collected daily. In developing CPS to enhance precision of animal management and interventions to enhance productivity it is first essential to determine the natural variation that exists in the system and to assess the potential for altering animal efficiency based on sensor input. During the first year of the study, cohorts of cattle at Virginia Tech will be subjected to efficiency screening to determine their responsiveness to real-time sensed indicators. Cattle (n=96) will be split into 4 cohorts of 24 animals, transitioned to Calan gates over a 14 day period and fed a standard lactation total mixed ration for a 35 d data collection period. Sensors will be placed in the rumen (for temperature and pH), on the neck (for rumination time, duration, and interval), in the feed bunk (feeding frequency, duration and amount), on the ankle (for speed frequency, and duration of movement) and in the milking equipment (for amount, composition and quality of milk). Using noted sensors, cows will be continuously monitored over the experimental period and data analyzed to determine the composite predictors of overall animal productive efficiency as milk production per unit of feed consumed. The emphasis for on-body sensors will be on incorporating wireless capability to avoid intrusion. White and Daniels will work closely together with Priya and Voyles to identify COTS sensor nodes that can be modified to include low-power wireless transceivers and with Sundaram to perform system identification and observability assessment to modify data gathered, as needed.Goal 1.2 (Voyles, Priya, Chiu, Sundaram). The objective of this sub-goal is to develop the structure of the generic animal models that will form the basis of decisions for action. As part of the decision layer, an animal-model-based structure will provide the abstraction that allows the architecture to apply to multiple farms and farm types (cow, sheep, poultry, etc). To estimate the state of each animal, discrete-time Kalman filters are employed with separate models for metabolic health and nutrition. These will be investigated below, with application to the cow model based on data from the above sub-goal.Goal 2.1 (Voyles, Priya, Min, Sundaram). The objective of this sub-goal is to construct a wireless networking infrastructure to support real-time precision animal agriculture. The goal of the wireless networking system is to adapt to varying demands of urgency for both data and executable programs (analytics) as they are routed between the core and the edge nodes. The ultimate goal seems simple: to move data from various layers in the network from the edge to the core of a cloud-based data storage platform to enable real-time decisions regarding the productivity, quality, safety, and efficiency of the farm and its products. Yet, because of the complexity of living entities, it is not so simple and will involve a novel collection of hardware layers, routing protocols, and distributed decision support across the system to support both top-down and bottom-up decisions and control.Goal 2.2 (Voyles, Priya, Sundaram, Chiu). The objective of this sub-goal is to integrate data obtained with the active sensing nodes into the network layer of the Generic Precision Animal Agriculture CPS.Co-PI Priya is exploring various active sensors that will test the CPS network interface protocol for edge analytics embedded in a purpose-built active sensor. Active sensing is not limited to robots or in-animal devices, but represents purpose-built sensors that actively gather data of specific interest, rather than passive sensors, such as security cameras. These sensors require computational capability and reside at the edge of the network, hence a protocol for "edge analytics" is necessary to integrate them modularly and robustly into the Precision Animal Agriculture reference architecture. In Year 1 and 2, the focus will be on collecting data from the passive sensor node with pH, temperature, and oxygen sensing capability. He will rely on commercially available wireless micro-sensors for pH, temperature, O2, and liquid density as a long term goal of the project is to map heterogeneity of microbe populations and fermentation parameters.?Goal 3.1 (Priya, Voyles, Donkin, White, and Daniels). The objective of this sub-goal is to build the new feed dispenser robot that will be used in the dairies.The new feed dispenser machine will be an integral part of the sustainable dairy CPS for both Virginia Tech and Purdue. The prototype will be developed at Virginia Tech as a stationary machine located in the freestall barn but will reside in campus laboratory space for initial testing. Figure 5 shows the current setup at Virginia Tech's milking station that provides the baseline platform. Upon entry into the station, a gate will close behind the cow preventing her egress for a minimum of 1 min. The cow's RFID ear tag and/or RFID leg tag will be read by an RFID reader. This will initiate autonomous action by the feed dispenser system. In addition to RFID, proximity sensors such as (LV-MaxSonar®-EZTM series) will be utilized for animal detection in order to improve accuracy. The LV-MaxSonar-EZ detects objects from 0-inches to 254-inches and provides sonar range information from 6-inches out to 254-inches with 1-inch resolution. Depending on individual animal needs, the cow will be delivered a specified quantity of feed supplement to support lactation needs or remedy acidosis, or both. The feed dispenser pan will contain load cells for automatic weighing of any feed refusals; weighbacks of uneaten feed will be weighed and discarded as the animal leaves the station. The machine will be modeled after construction of automatic dairy cow feed stations (e.g., Lely Cosmix; Lely, The Netherlands). Further, to entice cows to enter the station, a grooming roller will be installed over the cow chute (e.g. Lely Luna; Lely, The Netherlands). These rollers are popular and objectively enjoyed by cows.Goal 3.2 (Priya, Voyles, Donkin). The objective of this sub-goal is to replicate the new feed dispenser robot at Purdue.This is primarily an integration task, but deserves a separate line item as the prototype will be evaluated for ease of re-application.Goal 4. (White, Daniels, and Donkin) Evaluate the CPS research by deploying on two heterogeneous test farms During the third year of the study, two cohorts of animals (n=36 each; one at Virginia Tech and one at Purdue) will be fed in Calan gates to measure individual feed efficiency. Cows will undergo a 7 d gate training period, a 35 d screening period, and two experimental periods (14 d diet adaptation followed by 35 d treatment). Cattle will be partitioned into two groups. During the first experimental period, group 1 will be fed a standard lactation total mixed ration and group 2 will be fed a basal TMR supplemented according to the CPS algorithm for her response category. During the second experimental period, the treatments applied to the two groups will be switched. This will be replicated at VT and Purdue to evaluate the stability of cow sorting algorithms and feeding strategies defined in Obj 1.1, across institutions. In addition to providing greater numbers of animals the use of two locations will help to eliminate any potential bias based on the effects of the system, environment, or animals at a single location.

Progress 07/15/20 to 07/14/21

Outputs
Target Audience:The audience targeted during this reporting period was primarily fellow university research scientists and professionals at companies interested in precision animal agriculture. 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?We will conduct a final iteration of data collection for algorithm development, this time specifically focused on sensing the ruminal environment.

Impacts
What was accomplished under these goals? Under goal 1: We tested a second set of precision feeding algorithms in an on-farm test of whether a decision layer built upon sensed capacity on the farm at present could be used to influence feed efficiency of cattle in a meaningful way. Data for this project have been analyzed and the manuscript is in draft. The papers summarizing our first set of precision feeding algorithms tested are in review in the journal of dairy science presently. Under goal 2: Progress toward this objective is being made by collaborators at Purdue and Penn State. Under goal 3: Progress toward this objective is being made by collaborators at Purdue and Penn State. Under goal 4:Targets and key parameters that this new CPS system should consider have been cataloged while manually modeling what the CPS system would look like during our first data collection attempt. These targets and key parameters have been denoted in project meetings and will be used to iteratively update the planned design for the CPS. Progress toward this objective is being made by collaborators at Purdue and Penn State. A review paper summarizing some of the ruminal targets and key parameters for consideration is under review in the journal of dairy science at present.

Publications

  • Type: Journal Articles Status: Under Review Year Published: 2021 Citation: T.P. Price, V.C. de Souza, D.M. Liebe, M.D. Ellett, T.C. Davis, C.B. Gleason, K.M. Daniels, and R.R. White. 2021. Algorithm development for individualized precision feeding of supplemental top dresses to influence feed efficiency of dairy cattle. Journal of Dairy Science.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: 4. dos Reis, B.R.*, Z. Easton, D. Fuka, and R.R. White. 2021. A LoRa sensor network for monitoring pastured livestock location and activity. Translational Animal Science. DOI: https://doi.org/10.1093/tas/txab010
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: 12. dos Reis, B. R.*, Fuka, D. R., Easton, Z. M., & White, R. R. (2020). An open-source research tool to study triaxial inertial sensors for monitoring selected behaviors in sheep. Translational Animal Science, 4(4). doi:10.1093/tas/txaa188
  • Type: Journal Articles Status: Under Review Year Published: 2021 Citation: C.S. Han, U. Kaur, H. Bai, B.R. dos Reis, R.R. White, R.A. Nawrocki, R.M. Voyles, M.G. Kang, and S. Priya. 2021. Invited Review: Sensor technologies for real-time monitoring of the rumen environment. Journal of Dairy Science.
  • Type: Journal Articles Status: Under Review Year Published: 2021 Citation: T.P. Price, V.C. de Souza, D.M. Liebe, M.D. Ellett, T.C. Davis, C.B. Gleason, K.M. Daniels, and R.R. White. 2021. Short-term adaption of dairy cattle production parameters to individualized changes in dietary top dress. Journal of Dairy Science.


Progress 07/15/19 to 07/14/20

Outputs
Target Audience:The target audience of this project period was fellow scientists in the animal sciences, mechanical engineering, and electrical engineering fields. Efforts used to address this target audience included seminar presentations and preparation of peer-reviewed manuscripts. Changes/Problems:The COVID-19 pandemic delayed our ability to conduct animal research and limited our time in the lab to facilitate sensor development and network testing. As our labs have returned to normal, we are working diligently to return to our original timeline and are working with our respective universities to minimize disruptions to research if future lock downs are necessary. What opportunities for training and professional development has the project provided?The graduate students on this project have progressed to each leading the effort to develop review papers in their areas of expertise. This activity has enabled our team to maintain productivity while research efforts were slowed due to the COVID-19 pandemic and helped to give the graduate students leadership experience and broader their skills in coordinating interdisciplinary teams. Students have also been able to virtually attend conferences in their areas of interest, gaining exposure to the borader professional community and enhancing their skills in communicating research findings. How have the results been disseminated to communities of interest?Results have been disseminated to the communities of interest through presentaitons at the American Dairy Science Association annual meeting and through papers submitted to the Translational Animal Science journal. What do you plan to do during the next reporting period to accomplish the goals?Over the next reporting period we plan to conduct the next 2 planned animal trials, to continue sensor and model development exercises, to complete the review papers currently in draft, and to present findings to the broader scientific community through papers and conference presentations.

Impacts
What was accomplished under these goals? Goal 1: A second of four planned animal trials was conducted to develop databases for derivaiton of models predicting animal feeding requirements from performance and sensor data. Based on results from the first two animal experiments, additional efforts were made to generate wearable sensors for use in gathering additional, more precision individual animal information to inform models that would be more successful in making recommendations to improve efficiency of livestock with diet. Goal 2: In addition to work from our colleagues at Purdue and Penn State, we have worked on the development and refinement of LoRa-based sensors for livestock. This networking strategy meets many of our desired strategies, enhancing the sensing capacity of the CPS feedback loops developed in the research. Goal 3: Have developed and/or tested sensors for: animal motion/behavior; animal proximity; animal location; animal body temperature, respitration rate, and heart rate; local temperature, humidity, and pressure, as well as energy harvesting sensors measuring voltage harvested from animals under different activity, management and weather conditions. Goal 4: Our models developed based on the animal experiments suggest strong linkages between individual animal activity levels as well as milk composition with feed efficiency. This new knowledge supports more precision monitoring of individual dairy cattle to better understand the profile associated with more efficient cattle.

Publications

  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: S. Sujani, B. Wenner, J.L. Firkins and R.R. White (2020). A Network Analysis of Continuous Culture Fermentation Data. Abstracts of the 2020 American Dairy Science Association Annual Meeting, J of Dairy Sci., 103, pp: 160.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: B. R. dos Reis*, D. Fuka, Z. Easton, and R. R. White, 2020. An open-source microprocessor-based sensor for monitoring grazing animal behaviors. Abstracts of the 2020 American Dairy Science Association Annual Meeting
  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: B. R. dos Reis* and R. R. White. 2020. An integrated sensor network for monitoring pastured cattle health and location. Abstracts of the 2020 American Dairy Science Association Annual Meeting.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: B. R. dos Reis, B. Poudel, S. Priya, and R. R. White. 2020. Thermoelectric energy harvesting for wearable precision agriculture technologies. Abstracts of the 2020 American Dairy Science Association Annual Meeting. ,
  • Type: Journal Articles Status: Submitted Year Published: 2020 Citation: B.R. dos Reis, D.R. Fuka, Z.M. Easton, and R.R. White. 2020. Technical note: an open source research tool to study triaxial inertial sensors for monitoring selected behaviors in sheep. Translational Animal Science. TBD.


Progress 07/15/18 to 07/14/19

Outputs
Target Audience:The audience targeted during this reporting period was primarily fellow university research scientists and professionals at companies interested in precision animal agriculture. 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. What do you plan to do during the next reporting period to accomplish the goals?We will conduct the next iteration of data collection for algorithm development.

Impacts
What was accomplished under these goals? A set of4specific goalshave been identified for this project: Develop the decision layer of the generic precision animal agriculture CPS that incorporates flexible animal and herd models, guided by real data examples. Utilize existing data to test model parameterization strategies for feeding and health to reformulate supplement mixes based on real-time performance inputs representing both the individuals and the herd. Preliminary algorithms were tested for their ability to predict what cows should be fed to enhance their individual feed efficiencies. These algorithms differed in their ability to enhance efficiency depending on the feed being used as a supplement. A follow-up experiment to test improved algorithms is currently underway. Develop the network layer of the generic precision animal agriculture CPS, including body networking to extract embedded sensor data from individual animals; herd networking and tracking to consolidate and report individual and collective herd data with appropriate edge analytic capabilities; cloud computing capacity and software algorithms to receive, log, and interpret data from the edge to the core; and on-farm production interfaces to evaluate the production environment of the entire system. Progress toward this objective is being made by collaborators at Purdue and Penn State. Develop the physical layer artifacts for generic animal and herd networking as well as the purpose-built sensors and actuators for the dairy farm example CPS. Generic elements include an enhanced animal collar/mobility sensor and field-ready wireless access points. The purpose-built elements include an in-dwelling rumen sensor (developed separately, but this project is not dependent on its availability), automated feed delivery device, cow weight sensor, and in-line milk analysis equipment. Progress toward this objective is being made by collaborators at Purdue and Penn State. Develop new knowledge based on data networks that link animal and herd data with increased efficiency, profitability and animal well-being. Link the physical, network, and decision layers of the CPS and deploy, test and validate on-farm in two separate, networked installations. Targets and key parameters that this new CPS system should consider have been cataloged while manually modeling what the CPS system would look like during our first data collection attempt. These targets and key parameters have been denoted in project meetings and will be used to iteratively update the planned design for the CPS. Progress toward this objective is being made by collaborators at Purdue and Penn State.

Publications

  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Price, T.*, D. Liebe*, K. Daniels, R.R. White. 2019. Short-term adaptation of milk yield and feed efficiency to individualized dietary changes. 2019 ASAS-CSAS Annual Meeting and Trade Show. July 8th to 11th, 2019. Austin, TX
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: D.L. Liebe*, T.P. Price*, K.M. Daniels, R.R. White. 2019. Structural patterns in dairy cow efficiency responses to supplementation. Annual Meeting of the American Society of Animal Science. July 8th to 11th, 2019. Austin, TX.