Office Location
1675 Observatory Dr
438 Animal Sciences
Madison, WI 53706
Joao Dorea is an Assistant Professor in Precision Agriculture and Data Analytics. He obtained his BS (2008) in Agronomy from the Bahia State University (Brazil), MS (2010) and PhD (2014) degree in Animal Science from the University of Sao Paulo (Brazil). Dr. Dorea spent two years coordinating dairy and beef research in Latin America for DSM, a global supplier of animal health and nutrition. In 2019, he was hired as an Assistant Professor in the Department of Animal and Dairy Sciences (UW-Madison). Dorea develops research focused on digital technology and predictive analytics to optimize farm management decisions. His research group is interested in large-scale implementation of computer vision systems, wearable sensors, infrared spectroscopy, and other sensors to monitor animals in livestock farms. Examples of application include the use of digital technologies to monitor animal behavior, growth development, social interaction, and early detection diseases or animal welfare concerns. Such innovative research program has been extremely well accepted by the livestock industry and scientific community and has also attracted intramural and extramural funding.
Selected Peer-Reviewed Articles
Bresolin, T., R. Ferreira, F. Reyes, J. Van Os, and J. R. R. Dorea. 2022. Assessing optimal frequency for image acquisition in computer vision systems developed to monitor feeding behavior of group-housed Holstein heifers. Journal of Dairy Science. (in press). https://doi.org/10.3168/jds.2022-22138. “3”
Ferreira, R.E.P., T. Bresolin, G. J. M. Rosa, and J. R. R. Dorea. 2022. Using dorsal surface for individual identification of dairy calves through 3D deep learning algorithms. Computers and Electronics in Agriculture, 201, p.107272. https://doi.org/10.1016/j.compag.2022.107272 “3”
Caffarini, J. G., T. Bresolin, and J. R. R. Dorea. 2022. Predicting ribeye area and circularity in live calves through 3d image analyses of body surface. Journal of Animal Science, skac242. https://doi.org/10.1093/jas/skac24. “3”
Oliveira, D. A. B., L. G. R. Pereira, T. Bresolin, R. E. P. Ferreira, and J. R. R. Dorea. 2021. A review of deep learning algorithms for computer vision systems in livestock. Livestock Science, 253, 104700. “3”
Ribeiro, L. C., T. Bresolin, D. R. Casgrande, G. J. M. Rosa, M. A. C. Danes, J. R. R. Dorea. 2021. Disentangling data dependency using cross-validation strategies to evaluate prediction quality of cattle grazing activities using machine learning algorithms and wearable sensor data. Journal of Animal Science (in press). https://doi.org/10.1093/jas/skab206. “3”
Martin, M.J., J. R. R. Dorea, M. Borchers, R. Wallace, S. Bertics, S. Denise, K. Weigel, and H. M. White. 2021. Comparison of methods to predict feed intake and residual feed intake using behavioral and metabolite data in addition to classical performance variables. Journal of Dairy Science, 104(8):8765-8782. doi: 10.3168/jds.2020-20051. “3”
Silvi, R., L. G. R. Pereira, C. A. V. Paiva, T. R. Tomich, V. A. Teixeira, J. P. Sacramento, R. E. P. Ferreira, S. G. Coelho, F. S. Machado, M. M. Campos, J. R. R. Dorea. Adoption of Precision Technologies by Brazilian Dairy Farms: The Farmer’s Perception. Animals 2021, 11, 3488. https://doi.org/10.3390/ani11123488. “3”
Cairo, F. C., L. G. R. Pereira, M. Campos, T. R. Tomich, S. G. Coelho, C. F. A. Lage, A. P. Fonsecad, M. Borges, B. R. C. Alves, J. R. R. Dorea. 2020. Applying machine learning techniques on feeding behavior data for early estrus detection in dairy heifers. Computer and Electronics in Agriculture. 179-1:10.
Bresolin T, J. R. R. Dorea. Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems. Front Genet. 2020 Aug 20;11:923. doi: 10.3389/fgene.2020.00923. “3”
Aiken, V. C. F., A. F. A. Fernandes, T. L. Passafaro, J. S. Acedo, F. G. Dias, J. R. R. Dorea, G. J. M. Rosa. 2020. Forecasting beef production and quality using large scale integrated data from Brazil. Journal of Animal Science, 98:1-12. “3”
Dorea, J. R. R., V. N. Gouvêa, D. F. A. Costa, A. V. Pires, and F. A. P. Santos. 2020. Interactions between grazing management and a low-level of energy supplementation on intake and metabolism of beef cattle. Journal of Animal Science. In press. “5”
Aiken V. C. F., J. R. R. Dorea, J. S. Acedo, F. G. de Sousa, F. G. Dias, G. J. M. Rosa. 2019. Record linkage for farm-level data analytics: Comparison of deterministic, stochastic and machine learning methods. Computers and Electronics in Agriculture, 163:1-10. “3”
Fernandes, A. F. A., J. R. R. Dorea, R. Fitzgerald, W. Herring, G. J. M. Rosa. 2019. A novel automated system to acquire biometric and morphological measurements and predict body weight of pigs via 3D computer vision. Journal of Animal Science, 97:496-508. “3”
Cominotte, A., A. Fernandes, J. R. R. Dorea, G. J. M. Rosa, G. Pereira, M. Ladeira, E. Van Cleef. 2019. Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases. Livestock Science, 232:1-10. “3”
Dorea, J. R. R., G. M. J. Rosa, and L. E. Armentano. 2018. Mining data from milk infrared spectroscopy to improve feed intake predictions in lactating dairy cows. Journal of Dairy Science 101:5878-5889. “3”
Dorea, J. R. R., E. A. French, L. E. Armentano. 2017. Use of milk fatty acids to estimate plasma non-esterified fatty acid concentrations as an indicator of animal energy balance. Journal of Dairy Science, 100:6164-6176. “5”
Selected Book Chapters
Santos, F. A. P., R. S. Marques, and J. R. R. Dórea. 2016. Grain Processing for Beef Cattle. In: Millen D, De Beni Arrigoni M, Lauritano Pacheco R, editors. Rumenology. Cham: Springer; p. 213–241.
Selected Conference Proceedings
Dórea, J. R. R., F. A. P. Santos, and D. M. Shaefer. Grazing supplementation for pastured stocker cattle: meta-analysis. In: 2018 Driftless Region Beef Conference, 2018, Dubuque, IA, USA.
Santos, F. A. P., J. R. R. Dórea, J. de Souza, F. Batistel, and D. F. A. Costa. Forage Management and Methods to Improve Nutrient Intake in Grazing Cattle. In: 25th Annual Ruminant Nutrition Symposium, 2014, Gainesville, FL, USA. Ruminant Nutrition Symposium, 2014. p. 144-163.
Selected Popular Press Articles
Help us help you make better use of dairy data. Hoard’s Dairyman. February 10 2020.
Data: Think big, but start small. Hoard’s Dairyman. April 10 2020.
Big Data Wranglers. Grow Magazine. October 15 2019
Unlock udder complexities. PDPW. December 12 2019
Big Data, Big Opportunities. Progressive Dairy. July 29 2019
Selected Videos:
Agricultural Genome to Phenome Initiative – AG2PI
Challenges and opportunities of using computer vision systems for high-throughput phenotyping in dairy cattle https://www.youtube.com/watch?v=aJHeGV8Cjkg
Michigan State University Virtual Field Day – Precision Livestock Farming
Harnessing the Power of Computer Vision Systems to Improve Management Decisions in Livestock Operations https://www.youtube.com/watch?v=GkGFQ-fPXwE&t=1081s
HTCondor Week
ML, Image Analyses of Livestock Data https://agenda.hep.wisc.edu/event/1579/contributions/23069/attachments/7945/9004/Dorea_HTCondorWeek2021.mp4
UW-Madison Science Expedition
Artificial Intelligence in Animal Sciences https://www.youtube.com/watch?v=TDXGXr6v2O8
Undergraduate Courses
DY SCI 375 – Introduction to Digital Agriculture (3 credits, Fall)
Course Description: This three-credit course will focus on key concepts and applications of sensor technology and data analyses applied to livestock, environment, and crop production. In this course the students will (1) understand what precision agriculture is and why it is needed;(2) become familiar with data science principles; (3) learn the current remote sensing technologies in livestock and agricultural systems; (4) understand the principles and applications of sensor technology applied to animals, crop and environment; (5) become familiar with GIS (Geographic Information Systems) software; (6) gain a basic understanding of principles and applications of data analyses; (7) become familiar with cloud computing and data visualization; and (8) apply precision agriculture to a real situation.
Requirements: Prior coursework in MATH 112 and MATH 113 (or equivalent) and one Stats course (for example: STAT 301, STAT 371, or STAT 571)
Animal and Dairy Sciences Graduate and Research Committee
Alfred Toepfer Faculty Fellow Award
ADSA: Cargill Animal Nutrition Young Scientist Award