Joao Dorea

Links

https://scholar.google.com/citations?user=0_ASnjAAAAAJ
https://twitter.com/jrrdorea
https://www.instagram.com/digitallivestock/

Program Affiliations

Animal and Dairy Sciences
Biological Systems Engineering

Education and Certificates

M.S. 2010 – Animal Science, University of Sao Paulo
Ph.D. 2014 – Animal Science, University of Sao Paulo
Research Associate 2016-2019: Animal and Dairy Sciences, University of Wisconsin-Madison

Fields of Interest

• High-throughput animal phenotyping
• Computer vision systems for livestock
• Infrared spectroscopy (NIR and MIR)
• Machine learning for high-dimensional image data
• Multimodal sensor systems

Teaching

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)

Publications

• 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 Computer Vision Systems in Livestock. Livestock Science (in press). https://doi.org/10.1016/j.livsci.2021.104700.
• 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.
• 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.
• 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.
• 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.
• Passafaro, T. L., F. B. Lopes, J. R. R. Dorea, M. Craven, V. Breen, R. J. Hawken, and G. J. M. Rosa. 2020. Would large dataset sample size unveil the potential of deep neural networks for improved genome-enabled prediction of complex traits? The case for body weight in broilers. BMC Genomics. doi:10.21203/ rs.2.22198/v2.
• 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. Front. Vet. Sci. http://doi: 10.3389/fvets.2020.551269.
• 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.
• 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, 98:1-10. https://doi.org/10.1093/jas/skaa250.
• Cominotte, A., A. Fernandes, J. R. R. Dorea, G. J. M. Rosa, G. Pereira, M. Ladeira, E. Van Cleef. 2020. 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.
• Aiken V. C. F., J. R. R. Dórea J. R. R., 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: 2019, https://doi.org/10.1016/j.compag.2019.104857.
• Fernandes, A. F. A., J. R. R. Dórea, 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.
• Dórea, J. R. R., G. M. J. Rosa, 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. Dórea, H. Yang, 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.
• Dórea, 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.
• Dórea, 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.
• Chandler, T. L., R. S. Pralle, J. R. R. Dórea, S. E. Poock, G. R. Oetzel, R. H. Fourdraine, and H. M. White. 2017. Predicting hyperketonemia by logistic and linear regression using continuous milk and management variables in early lactation Holstein and Jersey cows. Journal of Dairy Science 101:2476–2491. https://doi.org/10.3168/jds.2017-13209.

Link for more publications:

https://scholar.google.com.br/citations?user=0_ASnjAAAAAJ&hl=en