Joao Dorea

About

Joao Dorea is an Assistant Professor in Biological Systems Engineering at the University of Wisconsin-Madison. He holds a Ph.D. in Animal Science from the University of Sao Paulo, obtained in 2014, and an M.S. in Animal Science from the same university, completed in 2010. His research focuses on high-throughput animal phenotyping, computer vision systems for livestock, infrared spectroscopy (NIR and MIR), machine learning for high-dimensional image data, and multimodal sensor systems. Dorea teaches a course on Digital Agriculture, which covers sensor technology, data analysis, remote sensing, GIS, cloud computing, and precision agriculture applications. His work involves developing and applying advanced data science and sensor technologies to livestock, environment, and crop production systems, contributing to the field through innovative research and education.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Engineering
• Computer vision
• Biology
• Telecommunications
• Environmental science
• Algorithm
• Biochemical engineering

Selected publications

• A review of deep learning algorithms for computer vision systems in livestock

  Livestock Science · 2021 · 150 citations

  Senior authorCorresponding
  DOI

• Image Analysis and Computer Vision Applications in Animal Sciences: An Overview

  Frontiers in Veterinary Science · 2020 · 148 citations

  Computer Vision, Digital Image Processing, and Digital Image Analysis can be viewed as an amalgam of terms that very often are used to describe similar processes. Most of this confusion arises because these are interconnected fields that emerged with the development of digital image acquisition. Thus, there is a need to understand the connection between these fields, how a digital image is formed, and the differences regarding the many sensors available, each best suited for different applicatio…

  DOI

• Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems

  Frontiers in Genetics · 2020 · 61 citations

  Senior authorCorresponding

  High-throughput phenotyping technologies are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. Collecting such individual-level information can generate novel traits and potentially improve animal selection and management decisions in livestock operations. One of the most relevant tools used in the dairy and beef industry to predict complex traits is infrared spectrometry, which is based on the analysis of…

  DOI

• International Symposium on Ruminant Physiology: Leveraging computer vision, large language models, and multimodal machine learning for optimal decision making in dairy farming

  Journal of Dairy Science · 2025-04-11 · 10 citations

  reviewOpen accessSenior author

  This article explores various applications of artificial intelligence (AI) technologies in dairy farming, including the use of computer vision systems (CVS) for animal identification, BCS and body shape analysis, and potential uses of large language models (LLM) in the dairy industry. Among recent advancements in precision livestock farming tools, CVS have gained popularity as powerful solutions for individual animal monitoring. These systems can capture phenotypes from multiple animals simultan…

  PublisherDOI

• Color-independent cattle identification using keypoint detection and Siamese neural networks in closed- and open-set scenarios

  Journal of Dairy Science · 2025-05-12 · 5 citations

  articleOpen accessSenior author

  Computer vision systems offer identification solutions for animals with distinct coat patterns but are less effective for solid-colored herds. Another complication in real-world conditions is when new animals are introduced or removed from the herd, representing a challenge known as the open-set scenario. A promising alternative for identifying solid-colored animals is to use keypoints, similar to recognition systems that use parts of the human face and body. However, body growth can alter biome…

  PublisherOA PDFDOI

Frequent coauthors

• Guilherme J. M. Rosa

  University of Wisconsin–Madison

  34 shared

• Flávio Augusto Portela Santos

  Hospital Universitário da Universidade de São Paulo

  23 shared

• Tiago Bresolin

  University of Illinois Urbana-Champaign

  22 shared

• Rafael Ferreira

  20 shared

• Vinícius N Gouvêa

  Texas A&M University System

  19 shared

• Arthur Francisco Araújo Fernandes

  13 shared

• Anderson de Moura Zanine

  11 shared

• Diogo Fleury Azevedo Costa

  Central Queensland University

  11 shared

Education

• Ph.D., Biological Systems Engineering

  University of Wisconsin–Madison

  2005

• M.S., Biological Systems Engineering

  University of Wisconsin–Madison

  2001

• B.S., Agricultural and Biological Engineering

  University of Wisconsin–Madison

  1999

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