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 products. He developed his expertise in data analytics and sensor technology when he came to the UW–Madison in 2016. He first worked with dairy nutritionists Dr. Lou Armentano and Dr. Dave Combs and later joined the lab of Dr. Guilherme Rosa, who specializes in agricultural applications of data analytics. In August of 2019, Dr. Dorea became a faculty in the Department of Animal and Dairy Sciences.
Dr. Dorea has been working extensively on the development of top-notch applications of artificial intelligence to optimize farm management decisions and improve animal nutrition and health. Dr. Dorea’s research focuses on the development of high-throughput phenotyping technologies, such as: (1) computer vision systems to improve health and growth development of beef and dairy cattle; (2) automated systems to monitor animal behavior; (3) predictive modeling using infrared spectroscopy data, and (4) natural language processing to automate data collection in agricultural systems. Such innovative research program has been extremely well accepted by the livestock industry and scientific community and has also attracted intramural and extramural funding (USDA AFRI-NIFA).
Selected Peer-Reviewed Articles
Martin, M. J., J. R. R. Dórea, M. R. Borchers, R. L. Wallace, S. J. Bertics, S. K. DeNise, K. A. 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. Accepted. https://doi.org/10.3168/jds.2020-20051.
Oliveira, D.A.B., L. G. R. Pereira, T. Bresolin, R. E. P. Ferreira. J.R.R. Dorea. 2021. A Review of Deep Learning Algorithms for Computer1Vision Systems in Livestock. Livestock Science (accepted).
Bresolin, T. and J. R. R. Dorea. 2020. Infrared Spectroscopy as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems. Frontiers in Genetics, 11:923, https://doi.org/10.3389/fgene.2020.00923.
Cairo, F. C., L. G. R. Pereira, T. R. Tomich, M. M. Campos, J. R.R Dorea. 2020. Applying machine learning techniques to individual feeding behavior data for early estrus detection in dairy heifers. Computer and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2020.105855
Fernandes, A. F. A., J. R. R. Dorea, R. Fitzgerald, W. Herring, G. J. M. Rosa. 2020. Comparison of data analytics strategies in computer vision systems to predict pig body weight, fat and muscle depths from 3D images. Journal of Animal Science, 98:1-9. https://doi.org/10.1093/jas/skaa250.
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. https://doi.org/10.1093/jas/skaa089.
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. https://doi.org/10.1093/jas/skaa163.
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. https://doi.org/10.1016/j.compag.2019.104857
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. https://doi.org/10.1093/jas/sky418.
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. https://doi.org/10.1016/j.livsci.2019.103904.
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. https://doi.org/10.3168/jds.2017-13997.
Donnelly, D. M., J. R. R. Dorea, H. Yang, and D. K. Combs. 2018. Technical note: Comparison of dry matter measurements from handheld near-infrared units with oven drying at 60°C for 48 hours and other on-farm methods. Journal of Dairy Science 101:9971-9977. https://doi.org/10.3168/jds.2017-14027.
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. https://doi.org/10.3168/jds.2016-12466.
Dorea, J. R. R., E. M. A. C. Danés, G. I. Zanton, and L. E. Armentano. 2017. Urinary purine derivatives as a tool to estimate dry matter intake in cattle: a meta-analysis. Journal of Dairy Science, 100:8977-8994. https://doi.org/10.3168/jds.2017-12908.
Dorea, J. R. R., and L. E. Armentano. 2017. Effects of common dietary fatty acids on milk yield and concentrations of fat and fatty acids in dairy cattle. Animal Production Science, 57:2224-2236. https://doi.org/10.1071/AN17335.
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
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
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
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