Source: VIRGINIA POLYTECHNIC INSTITUTE submitted to NRP
NRI: INT: COLLAB: RUMEN UNDERSTANDING THROUGH MILLIPEDE-ENGINEERED NAVIGATION AND SENSING (RUMENS)
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
COMPLETE
Funding Source
AFRI COMPETITIVE GRANT
Reporting Frequency
Annual
Accession No.
1018101
Grant No.
2019-67021-29007
Cumulative Award Amt.
$298,678.00
Proposal No.
2018-06098
Multistate No.
(N/A)
Project Start Date
Dec 1, 2018
Project End Date
Nov 30, 2022
Grant Year
2019
Program Code
[A7301]- National Robotics Initiative
Recipient Organization
VIRGINIA POLYTECHNIC INSTITUTE
(N/A)
BLACKSBURG,VA 24061
Performing Department
Animal and Poultry Science
Non Technical Summary
Just as NASA used the remotely operated vehicles (ROV) Spirit and Opportunity to gather and relay information that continues to broaden understanding of Mars, we will develop ROVs that will transform our knowledge of rumen biology and fermentation chemistry, with backing from NSF. The rumen is one of the primary digestive chambers in the stomach of a ruminant animal, such as a cow. Although the cow rumen is a very specific environment, improving our ability to study this ecosystem provides notable opportunity to enhance understanding of fermentation, food production, and energy generation, not just within cattle but within anaerobic fermentation environments in general. Rumen microorganisms are some of the world's most efficient fermenters of fibrous materials high in cellulose; however, only a fraction of the species in the rumen microbiome have been sequenced or cultured. The rumen ecosystem is a complex heterogeneous environment stratified vertically and horizontally that contains myriad specialized microclimates caused by differing density of feed particles and O2 concentrations, among other factors. These microclimates are believed to create optimal environments for unique microbial species that may have differing fermentation capacity and the stratification within the rumen may be one cause for difficulty in culturing many of these microorganisms outside the animal.A current roadblock hampering advancement in rumen physiology and microbiology is that contemporary research tools and methods lack the ability to achieve the necessary detail in measurement and sample collection. Key limitations of current technologies include: 1) lack of motility; 2) failure to self-identify location within the animal; 3) inability to retrieve samples for later analysis; and 4) poor precision and accuracy. For example, bolus-delivered temperature and pH probes can generate precise real-time measurements but sink to one location (typically in the reticulum, but ultimately unknown to the researcher) and output values can drift over time. A dynamic, precise and accurate "map" of the rumen environment through rumen ROV would provide an unprecedented understanding of this complex ecosystem. To address this gap, we will construct a robotic rumen ROV capable of nagivating, sampling, and characterizing the rumen and its contents.
Animal Health Component
30%
Research Effort Categories
Basic
70%
Applied
30%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
3023410101050%
3027210101050%
Knowledge Area
302 - Nutrient Utilization in Animals;

Subject Of Investigation
3410 - Dairy cattle, live animal; 7210 - Remote sensing equipment and technology;

Field Of Science
1010 - Nutrition and metabolism;
Goals / Objectives
The long-term goal is to use wireless remote sensing and sampling technologies to document spatial heterogeneity within the rumen and use this data across multiple animals and multiple herds to improve efficiency of ruminant feeding as well as advancing fermentation technologies across several applications and, eventually, across several animal species. The short-term goal of this project is to design and build a cattle-specific rumen ROV capable of navigating, sampling, and characterizing the rumen and its contents. To achieve our short-term goals, the specific objectives of the proposed research are:Design and build a cow rumen ROV,Evaluate the ROV as a remote sensing tool in a simulated rumen environment and in vivo,Demonstrate the ability of the ROV to collect real-time rumen data over long time scales,Demonstrate the ability of the ROV to collect rumen tissue and fluid samples over short time scales,Synthesize data to create maps and visualizations of a single cow rumen ecosystem,Agglomerate data from multiple cows to begin enabling scaling and generalization.
Project Methods
In this four-year program, we will design and build wireless rumen ROV (aims 1 &2), test the ROV and summarize our findings (aims 3 to 6).Objective 1: Design and build a cow rumen ROV: We will construct an ROV platform that is inspired by millipede locomotion to effectively move through the viscous rumen environment subject to significant digestive muscle contractions with no legs protruding from the body.The travelling wave is generated by operating two piezoelectric components with 90° phase offset.Piezoelectric actuators can be designed to provide the optimum mix of stroke and force.Objective 2: Design and build rumen sensing and sampling payloads: Co-PI Voyles will lead the development of the sensing and sampling payloads, assisted by Co-PIs Nawrocki and Newell who lend specific expertise in medical sensor development and piezoelectric actuator design.The main focus will be on collecting data from the suite of commercial sensors for pH, temperature, and gaseous oxygen concentration (when gas contact is available). We will rely on examples of commercially available micro-sensors such as pH (Vernier Software and Technology, LLC), temperature (Analog Devices, Inc. cat# ADT7420), O2 (SST Sensing; Coatbridge, Scotland), and liquid density (ISSYS; Ypsilanti, MI). We will compare the performance of currently utilized wireless rumen pH and temperature loggers (SmaXtec; Graz, Austria) with that available from Vernier and Analog Devices to decide upon the better performing component. The power source for the sensing platform will be primarily rechargeable lithium-ion batteries, which are commonly used inside animals in the sensing boluses, but we will include a wireless magnetic recharging module in the feeding station and milking parlor in order to provide long-term operation.Objectives 3 and 4: Demonstrate ROV Capacity as a Remote Sensing and Sample Collection Tool.To promote animal safety, all ROV capabilities will first be tested in a simulated rumen environment. Co-PIs White and Priya will complete this initial evaluation. White will obtain rumen contents and rumen tissue from either a local slaughterhouse or the necropsy lab at the Virginia-Maryland Regional College of Veterinary Medicine (VMRCVM). Priya's team will create a test rumen by placing rumen contents into a clear flexible plastic container and adhering tissue samples to one side of the container. The test rumen will be placed on an orbital shaker programmed to shake the synthetic rumen once every 30 seconds. This shaking will simulate regular mixing contractions within the rumen which may impede ROV navigation. A grid drawn on the outside of the testing vat will allow the ROV location and movement systems to be tested by blind navigation of the ROV around the container with charting of actual ROV movements against the reference grid. This evaluation of navigation will occur with and without the simulated mixing contractions to better understand how the ROV responds to and corrects for rumen motility.Live animal testing will occur at the Virginia Tech Dairy under the supervision of co-PI White. Although Virginia Tech, Penn State and Purdue all have cattle herds suitable for testing, Virginia Tech maintains the primary test herd for the CPS project, so we plan to use that herd for simplicity and cost efficiency. After IACUC approval, ruminally cannulated Holstein cows (n=4) will be used. Several cows with permanent rumen cannulae are maintained at Virginia Tech and are available for use.Objective 5: Construct maps of the rumen environment.After completing the testing phase, the ROV will embark on a grid search sensing mission through the rumen. The ROV will be navigated across 5 different horizontal strata (approximately 10 cm apart vertically) on a 10x10 cm grid. Rumen pH, temperature, O2 and density measurements will be collected at each intersection of the 10x10 cm navigation grid. Data collected in this grid search will be mapped to the corresponding location to generate a 3D visualization of the variation in the sensed metrics (1 graph per metric) across the physical locations of the rumen. A set of visualizations will be created for each of the 4 cannulated animals used.Objective 6: Aggregate data across animals.A comprehensive data storage network will be developed using cloud-based data handling to capture all outputs from the ROV. The storage network will provide a standardized data reporting platform to allow for aggregation of data across animals, laboratories, etc. A sample cross-animal analysis will be conducted using the verification experiments and the grid search data collected from the 4 cannulated animals.?

Progress 12/01/18 to 11/30/22

Outputs
Target Audience:The target audiences reached by our efforts during this reporting period included industry professionals, researchers in the engineering and animal sciences fields, and the general public. Changes/Problems:The produced rumen robot prototype was not capable of executing sensing tasks independently. Therefore, we used a simulated robot technique to generate the sensing data needed to complete the work proposed in the project. Now that we have a working version of the robot prototype and have identified concise sensing capacity needed (i.e., we can accomplish our target sensing with pH and temperature alone), we will explore strategies to leverage the pH sensing in future robotics work. What opportunities for training and professional development has the project provided?Graduate students assigned to the project have gained invaluable research skills in the planning and execution of interdisciplinary research as well as training in the appropriate methods (laboratory and statistical) necessary to generate and interpret the study results. Several undergraduate students were able to gain experience with research animals and laboratory methods through their roles as research assistants. Regarding professional development, graduate students benefited from a number of opportunities to present their study results to the scientific community at regional, national, and international animal science meetings. How have the results been disseminated to communities of interest?Results have been disseminated to producers through industry-oriented presentations at state Extension field days. Dissemination to the scientific connumity has taken place through presentation of research findings are scientific meetings such as the annual meeting of the American Society of Animal Science and the annual meeting of the American Dairy Science Association. Dissemination to the general public has occured through showcase presentations to Virginia Tech alumni during local outreach events. What do you plan to do during the next reporting period to accomplish the goals?Although this is our final report on this project, I would point out we have a number of papers on the in vivo testing that are yet to be published. We will continue to work toward publication of those papers over the next several months.

Impacts
What was accomplished under these goals? Design and build a cow rumen ROV, Several prototype robots were constructed and the power requirements to achieve sufficient thrust for navigation within the rumen environment were tested. A bio-inspired fish design was selected for subsequent testing. Evaluate the ROV as a remote sensing tool in a simulated rumen environment andin vivo, We constructed a synthetic rumen environment for testing the robotic protoype which can flexibly be adjusted to simulate the viscosity of the rumen fulid, the layers of fluid within the rumen, the chemical composition of that fulid or the motion of the fluid. These different elements of the environment can also be tested together. We tested the ability of the ROV to locomote within that environment, and explored how environmental parameters influenced the capacity of the ROV to travel, as measured by the rate of travel and the precision intraveling to a targeted point. Demonstrate the ability of the ROV to collect real-time rumen data over long time scales, Real-time sensing and long-term evaluation of the rumen environment was explored through sensing of pH, temperature, and dissolved CO2 concentrations. These data were collected for multiple fermentation cycles in animals exposed to diets verying in fermentability characteristics. These data suggest strong capacity to leverage pH to predict key fermentation parameters like rumen VFA profiles and levels throughout the fermentation. This finding will be further explored in future work. Demonstrate the ability of the ROV to collect rumen tissue and fluid samples over short time scales, The prototype ROV was not able to collect rumen tissue and fluid samples, however, the rumen fluid sensing successes deemed collection of these samples unnecessary. Synthesize data to create maps and visualizations of a single cow rumen ecosystem, We developed Bayesian Learning Networks of the rumen environment to explore causal inference among rumen fermentation indicators and mechanisms. These models show tremendous predictive capacity based on average fermentation data, and notable strength when real-time data are used for feeding. Agglomerate data from multiple cows to begin enabling scaling and generalization. Data from multiple cows outfitted with pH, temperature and dissolved CO2 sensing equipment were agglomerated and compared. Although different animals had different VFA profiles and extents of fermentation, the relationships identified between sensed parameters and VFA profiles were consistent across cows and diets, suggesting that these sensed metrics have high generalizability as a strategy for future study of the rumen ecosystem.

Publications

  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: S. Sujani* and R.R White, 2022. Effect of diet, rumen fermentation parameters and the combination on milk production/ feed efficiency. Proceedings of the Annual Meeting of the American Animal Science Association, USA.
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: A. Knoepfel, N. Liu, Y. Hou, S. Sujani*, B.R. Dos Reis*, R. White, K. Wang, B. Poudel, S. Gupta, and S. Priya. 2022. Development of tetrapod zinc oxide-based UV sensor for precision livestock farming and productivity. Biosensors. https://doi.org/10.3390/bios12100837
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: U. Kaur, R. Sriramdas, X. Li, X. Ma, A. Datta, B.R. dos Reis*, S. Sen, K. Daniels, R. White, R.M. Voyles, and S. Priya. 2022. Indwelling robots for ruminant health monitoring: a review of elements. Smart Agricultural Technology. 3:100109 https://doi.org/10.1016/j.atech.2022.100109
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: V. Souza , D.L. Liebe*, T.P. Price*, M.D. Ellet, T.C. Davis*, C.B. Gleason*, K.M. Daniels, and R.R. White. 2022. Algorithm development for individualized precision feeding of supplemental top dresses to influence feed efficiency of dairy cattle. J. Dairy Sci. https://doi.org/10.3168/jds.2021-20841
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Han, C.S., U. Kaur, B. Huiwen, B. Roqueto dos Reis*, R.R. White, R. Nawrocki, R. Voyles, M.G. Kang, and S. Priya. 2022. Invited Review: Sensor technologies for real-time monitoring of the rumen environment. J. Dairy Sci. https://doi.org/10.3168/jds.2021-20576
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Price, T.C.*, V.C de Souza , D.M. Liebe*, M.D. Elett, T.C. Davis*, C.B. Gleason*, K.M. Daniels, and R.R. White. 2021. Short-term adaptation of dairy cattle production parameters to individualized changes in dietary top dress. Animals. 11(12):3518. https://doi.org/10.3390/ani11123518
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: B.R. dos Reis*, S. Sujani*, and R. R. White. Additive Bayesian network for systems-oriented meta-analysis. 10th Workshop on Modelling Nutrient Digestion and Utilization in Farm Animals, 2022. Sardinia, Italy
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: B. R dos Reis*, C. B. Gleason**, and R. R. White. Bayesian network probabilistic modeling for understanding rumen dynamics. EAAP International Symposium on Energy and Protein Metabolism and Nutrition, 2022. Spain.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: S. Sujani*, C. B. Gleason**, B. R. dos Reis*, and R. R. White. Time-series assessment of rumen fermentation from meal-fed sheep. 10th Workshop on Modelling Nutrient Digestion and Utilization in Farm Animals, 2022. Sardinia, Italy.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: S. Sujani*, C.B Gleason**, B.R dos Reis* and R.R. White, 2022. Changes in rumen volatile fatty acid production in response to the varying degradability of fibre and protein. 7th EAAP International Conference, Spain.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: S. Sujani* and R.R White, 2022. Composition of milk as explained by rumen fermentation parameters, diet composition and combination: a meta-analysis. Proceedings of the Annual Meeting of the American Animal Science Association, USA.


Progress 12/01/20 to 11/30/21

Outputs
Target Audience:The target audiences reached by our efforts during this reporting period included industry professionals, researchers in the engineering and animal sciences fields, and the general public. Changes/Problems:The COVID-19 pandemic delayed the protoyping of the robot at Penn State. As such, we had to purchase commercial data loggers and sensors to begin collecting preliminary data we originally had anticipated the robot to collect. In the coming months, we expect to leverage that data into publications and into more targeted and informed use of the robot in initiial in vivo testing. What opportunities for training and professional development has the project provided?Graduate students assigned to the project have gained invaluable research skills in the planning and execution of interdisciplinary research as well as training in the appropriate methods (laboratory and statistical) necessary to generate and interpret the study results. Several undergraduate students were able to gain experience with research animals and laboratory methods through their roles as research assistants. Regarding professional development, graduate students benefited from a number of opportunities to present their study results to the scientific community at regional, national, and international animal science meetings. How have the results been disseminated to communities of interest?Results have been disseminated to producers through industry-oriented presentations at state Extension field days. Dissemination to the scientific connumity has taken place through presentation of research findings are scientific meetings such as the annual meeting of the American Society of Animal Science and the annual meeting of the American Dairy Science Association. Dissemination to the general public has occured through showcase presentations to Virginia Tech alumni during local outreach events. What do you plan to do during the next reporting period to accomplish the goals?During the next reporting period we plan to progress to in vivo testing of the ROV prototype.

Impacts
What was accomplished under these goals? The following was accomplished under these goals: Design and build a cow rumen ROV, A prototype robot has been constructed and the power requirements to achieve sufficient thrust for navigation within the rumen environment were tested. A site test of the prototype in a simulated rumen environment is underway. Evaluate the ROV as a remote sensing tool in a simulated rumen environment andin vivo, We constructed a synthetic rumen environment for testing the robotic protoype which can flexibly be adjusted to simulate the viscosity of the rumen fulid, the layers of fluid within the rumen, the chemical composition of that fulid or the motion of the fluid. These different elements of the environment can also be tested together. Demonstrate the ability of the ROV to collect real-time rumen data over long time scales, We have a trial planed for Spring 2022 to conduct this objective, pending the arrival of the most recent robotic prototype. Due to delays in the prototyping process, we purchased a commercially available sensor system in 2021 and began testing the spatial heterogeneity of the rumen environment with that system. These results are currently being analyzed and publications are expected in 2022. Demonstrate the ability of the ROV to collect rumen tissue and fluid samples over short time scales, We have a trial planed for Spring 2022 to conduct this objective, pending the arrival of the most recent robotic prototype.Due to delays in the prototyping process, we purchased a commercially available sensor system in 2021 and began testing the temporal heterogeneity of the rumen environment with that system. These results are currently being analyzed and publications are expected in 2022. Synthesize data to create maps and visualizations of a single cow rumen ecosystem, In light of delays in the robot construction and ptotyping process, we have collected literature data to begin modeling the rumen environment in more detail before we advance to models based on rumen sensed metrics. We are currently evaluating and comparing models and publications are expected in 2022. Agglomerate data from multiple cows to begin enabling scaling and generalization. We expect to conduct this wokr after experimental testing in summer of 2022.

Publications

  • Type: Journal Articles Status: Accepted Year Published: 2022 Citation: Han, C.S., U. Kaur, B. Huiwen, B. Roqueto dos Reis, R.R. White, R. Nawrocki, R. Voyles, and S. Priya. 2022. Invited Review: Sensor technologies for real-time monitoring of the rumen environment. J. Dairy Sci.
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Gleason, C.B.*, L.M. Beckett*, B. Roqueto dos Reis*, and R.R. White. 2021. Evaluating the relationship between in vitro and in situ starch degradation rates. Animal Feed Science and Technology. 115175.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Sujani, S.*, Wenner, B., Firkins, J. L., & White, R. R. (2020). A network analysis of continuous culture fermentation data.. In JOURNAL OF DAIRY SCIENCE Vol. 103 (pp. 160). ELECTR NETWORK: ELSEVIER SCIENCE INC. Retrieved from http://gateway.webofknowledge.com/
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: dos Reis, B. R.*, & White, R. R. (2020). An integrated sensor network for monitoring pastured cattle health and location.. In JOURNAL OF DAIRY SCIENCE Vol. 103 (pp. 237-238). ELECTR NETWORK: ELSEVIER SCIENCE INC. Retrieved from http://gateway.webofknowledge.com/


Progress 12/01/19 to 11/30/20

Outputs
Target Audience:The target audiences reached by our efforts during this reporting period included industry professionals, researchers in the engineering and animal sciences fields, and the general public. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Graduate students assigned to the project have gained invaluable research skills in the planning and execution of interdisciplinary researchas well as training in the appropriate methods (laboratory and statistical) necessary to generate and interpret the study results. Several undergraduate students were able to gain experience with research animals and laboratory methods through their roles as research assistants. Regarding professional development, graduate students benefited from a number of opportunities to present their study results to the scientific community at regional, national, and international animal science meetings. How have the results been disseminated to communities of interest?Results have been disseminated to producers through industry-oriented presentations at state Extension field days. Dissemination to the scientific connumity has taken place through presentation of research findings are scientific meetings such as the annual meeting of the American Society of Animal Science and the annual meeting of the American Dairy Science Association. Dissemination to the general public has occured through showcase presentations to Virginia Tech alumni during football weekends and at local alumni events. What do you plan to do during the next reporting period to accomplish the goals?During the next reporting period we plan to progress to in vivo testing of the ROV prototype.

Impacts
What was accomplished under these goals? Design and build a cow rumen ROV, A working model of the cow rumen ROV was designed and fabricated. A number of different locomotion strategies were developed, of which a longitudinal wave motion approach was identified as the most ideal and was implemented into the designed and fabricated ROV. A novel pH sensor was also fabricated which can be added into the cow rumen ROV to monitor ruminal pH. The fabricationand testing of the ROV was hindered significantly by the COVID-19 pandemic because the engineering laboratory facilities at Penn State were closed for several months. Evaluate the ROV as a remote sensing tool in a simulated rumen environment andin vivo, In vitro assessments of the ROV were conducted in water, geletin and simulated rumen fluid. These tests were conducted to evaluate necessary improvements in the longitudinal wave motion mechanism likely to be necessary prior to successful in vivo testing. In vitro testing of the novel pH sensor was also conducted and the sensor demonstraated excellent repeatability and linearity. In vivo testing is planned for the 2021 calendar year. Demonstrate the ability of the ROV to collect real-time rumen data over long time scales, Due to delays in the robot fabircation caused by COVID-19 we have not progressed to in vivo testing yet. This is planned for 2021. Demonstrate the ability of the ROV to collect rumen tissue and fluid samples over short time scales, Due to delays in the robot fabircation caused by COVID-19 we have not progressed to in vivo testing yet. This is planned for 2021. Synthesize data to create maps and visualizations of a single cow rumen ecosystem, Due to delays in the robot fabircation caused by COVID-19 we have not progressed to in vivo testing yet. This is planned for 2021. Agglomerate data from multiple cows to begin enabling scaling and generalization. Due to delays in the robot fabircation caused by COVID-19 we have not progressed to in vivo testing yet. This is planned for 2021.

Publications

  • Type: Journal Articles Status: Published Year Published: 2020 Citation: 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: Published Year Published: 2020 Citation: Gleason, C. B.*, White, R. R., & Schramm, H. H. (2020). Influence of two rumen cannulation techniques on postoperative recovery in sheep. VETERINARY SURGERY, 11 pages. doi:10.1111/vsu.13547
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: 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: Souza, V. and R.R. White. 2021. Variation in urea kinetics associated with ruminant species, dietary characteristics, and ruminal fermentation: A meta-analysis. Journal of Dairy Science. DOI: https://doi.org/10.3168/jds.2020-19447
  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Sujani, S.*, Wenner, B., Firkins, J. L., & White, R. R. (2020). A network analysis of continuous culture fermentation data.. In JOURNAL OF DAIRY SCIENCE Vol. 103 (pp. 160). ELECTR NETWORK: ELSEVIER SCIENCE INC. Retrieved from http://gateway.webofknowledge.com/
  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: dos Reis, B. R.*, & White, R. R. (2020). An integrated sensor network for monitoring pastured cattle health and location.. In JOURNAL OF DAIRY SCIENCE Vol. 103 (pp. 237-238). ELECTR NETWORK: ELSEVIER SCIENCE INC. Retrieved from http://gateway.webofknowledge.com/
  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: dos Reis, B. R.*, Poudel, B., Priya, S., & White, R. R. (2020). Thermoelectric energy harvesting for wearable precision agriculture technologies.. In JOURNAL OF DAIRY SCIENCE Vol. 103 (pp. 238). ELECTR NETWORK: ELSEVIER SCIENCE INC. Retrieved from http://gateway.webofknowledge.com/


Progress 12/01/18 to 11/30/19

Outputs
Target Audience:The target audience during this reporting period was fellow scientists. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The project has provided the graduate students the opportunity to learn, collaborate, and innovate outside their disciplines. They interact as a student group weekly and with the broader faculty group on a monthly basis. Students have also had the opportunity to attend professional society conferences to present research. How have the results been disseminated to communities of interest?Results have been disseminated to the communities of interest through presentations at profesional society conferences. What do you plan to do during the next reporting period to accomplish the goals?We plan to finalize the prototype ROV for in vivo testing. We anticipate needing another round of design refinement after the in vivo testing, which we hope to also accomplish next year so that the remaining project years can be used for data collection.

Impacts
What was accomplished under these goals? Design and build a cow rumen ROV, Several prototype romen ROV have been constructed Different locomotion strategies have been tested A downsized piezoelectric motor is being developed to facilitate downscaling The prototype is being updated with more thrust (per activities under objective 2) and will be outfitted with sensors for in vivo testing in 2020. Evaluate the ROV as a remote sensing tool in a simulated rumen environment andin vivo, The locomation activity of the test ROV has been evaluated in a gelatin-based medium. The test identified the longtiudinal locomotion activity had insufficient thrust to effectively move through the more dense medium. Pending updates to the robot, in vivo testing is expected for 2020. Demonstrate the ability of the ROV to collect real-time rumen data over long time scales, Anticipated 2021 Demonstrate the ability of the ROV to collect rumen tissue and fluid samples over short time scales, Anticipated 2021 Synthesize data to create maps and visualizations of a single cow rumen ecosystem, Anticipated 2022 Agglomerate data from multiple cows to begin enabling scaling and generalization. Anticipated 2022

Publications

  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2020 Citation: Roqueto dos Reis, B., and R.R. White. 2020. Thermoelectric Energy Harvesting For Wearable Precision Agriculture Technologies. Annual Meeting of the American Dairy Science Association.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2020 Citation: B. R. dos Reis, B. Poudel, S. Priya, and R. R. White. 2020. An Integrated Sensor Network for Monitoring Pastured Cattle Health and Location. Annual Meeting of the American Dairy Science Association.