Brief Summary of Minutes of Annual Meeting

NC1211 had our first formal meeting this spring.  A poll was distributed among participants ahead of the meeting to decide on face to face or virtual meeting and all the potential attendant indicated that they could only participate online.

Thus, the meeting was held virtually on September 7^th from 12 noon to 4:30 PM. A total of 14 participants attended (listed above) and the agenda was the following:

12:00 PM         Introduction - Archie Clutter (Advisor; UNL); Christina Hamilton (NCRA)

12:15 PM         Presentation 1 – Juan Steibel (Iowa State Univ.)

12:35 PM         Presentation 2 – Essam Abdelfattah (UC-Davis)

12:55 PM         Presentation 3 – Madonna Benjamin (Michigan State Univ.)

1:15 PM           Presentation 4 – Joao Dorea (Univ. of Wisconsin-Madison)

1:35 PM           Presentation 5 – Jeremiah Davis (Auburn University)

1:55 PM           Presentation 6 – Guilherme Rosa (Univ. of Wisconsin-Madison)

2:15 PM           Break

2:45 PM           Business Meeting

4:30 PM           Close

The goals of our meeting were to 1) introduce new members, 2) update on progress by participating stations, 3) conduct business meetings with continuation plans.

We started the meeting with brief individual introductions, four new participants were introduced. Six participants from five stations presented updates.

Official Actions:

Juan Steibel was elected chair, Joao Dorea was elected secretary.  The 2023 meeting will be held during the USPLF meeting in Knoxville Tennessee, Sunday May21st.

Activities:  Members of the NC1211 have worked together to create the second USPLF conference to be held Knoxville, TX May 22-25.  This will allow increase visability of precision animal management research and highlight the needs to producers and current technology through the producer and industry panels.

In addition, several members have teamed up to secure grant funding:

University of Nebraska-Lincoln/University of Illinois Shi, Condotta, Brown-Brandl FACT-AI: Cyberinformatic Tools for Exploring and Validating Sow Posture and Piglet Activity

University of Michigan, University of Nebraska-Lincoln, University of Wisconsin, Katholic University of Leuven Steibel, J., Brown-Brandl, T., Rosa, Siegford, Psota, Dorea, Morris, Benjamin, and Norton FACT-CIN: A Coordinated Innovation Network for Advancing Computer Vision in Precision Livestock Farming

Iowa State University, University of Nebraska-Lincoln, University of Kentucky, Ramirez, Hoff, Harmon, Brown-Brandl, T., Hayes, Rohrer CARE: Modern pigs urgently need facilities with modern ventilation: Updating swine ventilation standards/guidelines

Morris, D., Benjamin M., Brown-Brandl, T. Sharma, S.R. and Rohrer, G., FACT-CIN: Swine health and growth monitoring

Milestones:

(2022): Recruit members from experiment stations to participate in the objective and tasks delineated to ensure deep expertise. Complete inventory of past, ongoing, and future work in each objective; identify commonalities and areas for synergy.

 Membership went up from 10 members in the foundational group of NCDC 235 to 47 current members in this first year of NC1211. Moreover, a total of 25 institutions are represented including LGU experimental stations and industry. Current collaborative projects between members of NC1211 include animal scientists, engineers and computer scientists. Thus, we consider that this milestone has been accomplished.

1. Benicio, L. M. ,K. O. S. Miranda, T. M. Brown-Brandl, I. C. F. S. Condotta, Y. Xiong. 2022. Obtaining broiler chickens’ weight through depth image In10th European Conference on Precision Livestock Farming, ECPLF 2022 Vienna, Vienna, Austria. 151-158.
2. Brown-Brandl, Tami & Hayes, Morgan & Rohrer, Gary & Eigenberg, Roger. (2023). Thermal comfort evaluation of three genetic lines of nursery pigs using thermal images. Biosystems Engineering. 225. 1-12. 10.1016/j.biosystemseng.2022.11.002.
3. Codling JR, Dong Y, Bonde A, Bannis A, Macon A, Rohrer G, Miles J, Sharma S, Brown-Brandl T, Noh HY, Zhang P. 2022 Sow posture and feeding activity monitoring in a farrowing pen using ground vibration. In 10th European Conference on Precision Livestock Farming ECPLF, 2022, Vienna, Austria. p 240-248
4. Ferreira, R. E., Bresolin, T., Rosa, G. J., & Dórea, J. R. (2022). Using dorsal surface for individual identification of dairy calves through 3D deep learning algorithms. Computers and Electronics in Agriculture, 201, 107272.
5. Han, J., Dorea, J. R., Norton, T., Parmiggiani, A., Morris, D., & Siegford, J. and Juan P. Steibel. DEVELOPMENT AND ASSESSMENT OF PREDICTIVE MODELS FOR IMPROVED SWINE FARMING, 68.
6. Han, J., Siegford, J., Colbry, D., Lesiyon, R., Norton, T., Chen, C., & Steibel, J. P. (2022). 10 Deep Learning for Multi-Behavioral Video Classification of Interactive Behaviors of Pigs in Single-Spaced Automatic Feeders. Journal of Animal Science, 100(Supplement_2), 13-13.
7. Han, J., Siegford, J., de los Campos, G., Tempelman, R. J., Gondro, C., & Steibel, J. P. (2022). Analysis of social interactions in group-housed animals using dyadic linear models. Applied Animal Behaviour Science, 256, 105747.
8. Macon, A. P., Sharma, S., Vander Woude, E., Lee, B., Markvicka, E., Rohrer, G., & Brown-Brandl, T. (2022). Behavioral Time Budgets for Sows Before and After Farrowing. In 2022 ASABE Annual International Meeting (p. 1). American Society of Agricultural and Biological Engineers.
9. Norton, T., Brown Brandl, T., Panogakis, P., Cruz, V., Diefes-Dux, H., & Calvet, S. (2022). Educating for Precision Livestock Farming: The knowledge, skills and abilities to meet future industry and societal needs. Precision Livestock Farming'22, 186-192.
10. Pacheco, V. M., Brown-Brandl, T. M., Sharma, S., Sousa, R. V., Rohrer, G., Martello, L. S. Posture detection of sows housed in farrowing crates using composite image models. In 10th European Conference on Precision Livestock Farming ECPLF, 2022 Vienna, Austria p. 267-275.
11. Pacheco, V. M., Sousa, R. V., Sardinha, E. J., Rodrigues, A. V., Brown-Brandl, T. M., & Martello, L. S. (2022). Deep learning-based model classifies thermal conditions in dairy cows using infrared thermography. Biosystems Engineering, 221, 154-163.
12. Perttu, R. K., Peiter, M., Bresolin, T., Dórea, J. R. R., & Endres, M. I. (2022). Feeding behaviors collected from automated milk feeders were associated with disease in group-housed dairy calves in the Upper Midwest United States. Journal of Dairy Science, S0022-0302.
13. Ramirez, B. C., Hayes, M. D., Condotta, I. C., & Leonard, S. M. (2022). Impact of housing environment and management on pre-/post-weaning piglet productivity. Journal of animal science, 100(6), skac142.
14. Ramirez, B. C., Hoff, S. J., Hayes, M. D., Brown-Brandl, T. M., Harmon, J. D., & Rohrer, G. A. (2022). A review of swine heat production: 2003 to 2020. Frontiers in Animal Science. doi: 10.3389/fanim.2022.908434 Dept of Agriculture-NIFA
15. Sharma, S., Brown-Brandl, T., Rohrer, G., Rempel, L., Ostrand, L., & Mote, B. (2022). Lameness detection in sows using few-shot approach. In10th European Conference on Precision Livestock Farming, ECPLF 2022 Vienna, Vienna, Austria. p. 284-292.
16. Steibel J.P., J. Han, C. Chen, J. Siegford, T. Norton, D. (2022) Validation of computer vision algorithms for classifying video segments applied to behavioural phenotyping of pigs. Proceedings of the 12th World Congress on Genetics Applied to Livestock Production.
17. Steibel, J. P., T. Brown-Brandl, G. J. M. Rosa, J. M. Siegford, E. Psota, M.Benjamin, D. Morris, J. R. R. Dorea, T. Norton 2022 Coordinated innovation network for advancing Computer Vision in Precision Livestock Farming In 10th European Conference on Precision Livestock Farming, ECPLF 2022  Vienna, Austria p. 1030-1036.
18. Teixeira, V. A., Lana, A. M. Q., Bresolin, T., Tomich, T. R., Souza, G. M., Furlong, J., ... & Pereira, L. G. R. (2022). Using rumination and activity data for early detection of anaplasmosis disease in dairy heifer calves. Journal of Dairy Science, 105(5), 4421-4433.
19. Trenhaile-Grannemann, M., Y. Xiong, W. Z. Liang, T. M. Brown-Brandl, K. Stalder, B. E. Mote, D. R. Obermier, S. G. Millburn 2022. Utilizing imaging methodologies to classify sow characteristics for optimized selection.  In 10th European Conference on Precision Livestock Farming, ECPLF 2022  Vienna, Austria p 409-416.
20. Wurtz, K., Norton, T., Siegford, J., & Steibel, J. (2022). Chapter 13: Assessment of open-source programs for automated tracking of individual pigs within a group. In Practical Precision Livestock Farming: Hands-on experiences with PLF technologies in commercial and R&D settings (pp. 213-230). Wageningen Academic Publishers.
21. Xiong, Y. , E. T. Psota, T. M. Brown-Brandl, B. Mote, T. B. Schmidt, G. E. Erickson. 2022. A prototype imaging method for feed estimation in beef cattle 2022. In 10th European Conference on Precision Livestock Farming, ECPLF 2022 Vienna, Vienna, Austria. p. 231-238.