Credit hours
In-class work per week |
Practice per week |
Credits |
Duration |
Total |
28 |
2 |
4 |
2 weeks |
60 hours |
Instructor
Flavio Augusto Portela Santos
Objective
This course will cover key concepts and applications of sensor technology and artificial intelligence
applied to livestock and companion animals. At completion of this course, students should be able to:
• Understand what precision livestock is and why it is needed
• Become familiar with data science and artificial intelligence principles
• Learn the current sensor sensing technologies used in livestock and companion animals
• Explain principles and applications of sensor technology applied to animals
• Become familiar with artificial intelligence applications in veterinary medicine
• Understand the major ethic concerns associated with Artificial Intelligence for agriculture
Content
1) Introduction to Artificial Intelligence and Sensing Technology; (2) Wearable sensors for monitoring farm animals; (3) Technologies for monitoring animal behavior and location; (4) Computer vision to monitor animal behavior and health; (5) Acoustic techniques to assess animal health; (6) Ultrasound image analyses in veterinary medicine; (7) AI Technology: food quality and consumer experience; (8) Drones for Pasture Management. and Animal Monitoring; (9) Sensors for monitoring of animal estrus/fertility/pregnancy; (10) Entrepreneurship in AgTech; (11) Robotic milking systems; (12) Tech. for grazing behavior and automated grazing management; (13) Sensors to monitor animal feed; (14) Ethics in AI and Data Science.
Bibliography
Oliveira, D. B. O., L. G. R. Pereira, T. Bresolin, R. E. P. Ferreira, J. R. R. Dorea. 2021. A Review of Deep Learning Algorithms for Computer Vision Systems in Livestock. Livestock Science, 253, 1:15. https://doi.org/10.1016/j.livsci.2021.104700
Bresolin T., J. R. R. Dorea. 2020. Infrared Spectroscopy as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems. Frontiers in Genetics, 11-2:20. https://doi.org/10.3389/fgene.2020.00923.
Fernandes, A. F. A., J. R. R. Dorea, G. J. M. Rosa. 2020. Image Analysis and Computer Vision Applications in Animal Sciences: An Overview. Frontiers in Veterinary Science, 11-2:20. Frontiers in Veterinary Science. https://doi.org/10.3389/fvets.2020.551269
Higaki, S., Menezes, G.L., Ferreira, R.E., Negreiro, A., Cabrera, V.E. and Dorea, J.R.R., 2024. Objective dairy cow mobility analysis and scoring system using computer vision-based keypoint detection technique from top-view 2D videos. Journal of Dairy Science.
Ferreira, R.E., de Luis Balaguer, M.A., Bresolin, T., Chandra, R., Rosa, G.J., White, H.M. and J.R.R. Dorea. 2024. Multi-modal machine learning for the early detection of metabolic disorder in dairy cows using a cloud computing framework. Computers and Electronics in Agriculture, 227, p.109563.
Tuggle, C. K., Clarke, J. L., Murdoch, B. M., E. Lyons, N. M. Scott, B. Benes, J. D. Campbell, S. Das Choudhury, H. Chung, C. L. Daigle, J. C. M. Dekkers, J. R. R. Dorea et al. 2024. Current challenges and future of agricultural genomes to phenomes in the USA. Genome Biology. 25:8. https://doi.org/10.1186/s13059-023-03155-w
Siegford, J. M., J. P. Steibel, J. Han, M. Benjamin, T. Brown-Brandl, J. R. R. Dorea, D. Morris, T. Norton, E. Psota, G. J. M. Rosa. 2023. The quest to develop automated systems for monitoring animal behavior. Applied Animal Behavior Science. 265:106000.