Angela Green-Miller

About

Angela Green-Miller is associated with the Center for Digital Agriculture at the University of Illinois. The center focuses on research and development in digital and precision agriculture, including AI-driven tools, data collection, and analysis to optimize agricultural practices. The center offers interdisciplinary programs such as a Master’s Degree in Engineering with a concentration in Digital Agriculture, and collaborates internationally through initiatives like the Global Perspectives on Digital/Smart Agriculture Seminar Series. The Center for Digital Agriculture is engaged in projects like AI AgriBench, CropWizard, and the PigLife Dataset, which aim to build trust in AI applications in agronomy, provide decision-support services for agricultural professionals, and develop benchmarks for computer vision systems in livestock industries. The center also hosts events such as conferences, hackathons, and seminars to promote innovation and knowledge exchange in digital agriculture. Its efforts include supporting research, education, and industry partnerships to advance the adoption of AI and digital technologies in agriculture.

Research topics

• Computer Science
• Biology
• Animal science
• Ecology
• Mathematics
• Business
• Data science
• Engineering
• Psychology
• Systems engineering

Selected publications

• Barriers to computer vision applications in pig production facilities

  Computers and Electronics in Agriculture · 2022 · 38 citations

  PublisherDOI

• Evaluation of sow thermal preference across three stages of reproduction

  Journal of Animal Science · 2021 · 28 citations

  The metabolic heat production of modern pigs has increased by an average of 16%, compared with sows of 30 years ago. Therefore, it is likely that temperature recommendations require updating to meet the needs of modern pigs. The objective of this study was to evaluate whether different reproductive stages of sows altered thermal preference and if current recommendations required updating. Twenty multiparous sows (3.4 ± 1.2 parity) in different reproductive stages (nonpregnant: n = 7; mid-gestati…

  PublisherOA PDFDOI

• Thermoregulatory and physiological responses of nonpregnant, mid-gestation, and late-gestation sows exposed to incrementally increasing dry bulb temperature

  Journal of Animal Science · 2021 · 14 citations

  Gestating sows may be more susceptible to increasing dry bulb temperatures (TDB) due to greater metabolic heat production and increased body mass, especially as gestation advances. However, there are few studies on the thermoregulatory and physiological responses of sows at differing gestation stages exposed to gradually increasing temperatures. The study objective was to determine the thermoregulatory and physiological responses of nonpregnant (n = 12; parity 3.27 ± 0.86), mid-gestation (59.7 ±…

  PublisherOA PDFDOI

• AIFARMS: Artificial intelligence for future agricultural resilience, management, and sustainability

  AI Magazine · 2024-02-22 · 12 citations

  articleOpen access

  Abstract The AIFARMS Artificial Intelligence for Future Agricultural Resilience, Management, and Sustainability national AI institute brings together over 40 world‐class AI and agriculture researchers, with the common mission to develop foundational advances in AI and use them to ensure that future agriculture is environmentally friendly, sustainable, affordable, and accessible to diverse farming communities. Since its establishment in 2020, AIFARMS has advanced the state of the art in autonomou…

  PublisherOA PDFDOI

• Promote computer vision applications in pig farming scenarios: High-quality dataset, fundamental models, and comparable performance

  Journal of Integrative Agriculture · 2024-08-23 · 12 citations

  articleOpen accessSenior author

  Computer vision is widely recognized as an influential technology in the field of precision management of animals. Emerging studies have demonstrated the potential to improve pig health and welfare through animal surveillance systems and computer vision (CV) algorithms. However, the lack of benchmark datasets and robust fundamental algorithms restrict CV applications for the commercial use. This study aims to bridge the gap between technology development and commercial applications in pig farmin…

  PublisherDOI

Frequent coauthors

• Richard S. Gates

  Iowa State University

  28 shared

• Daniel W Shike

  University of Illinois Urbana-Champaign

  17 shared

• Jay S Johnson

  Agricultural Research Service

  12 shared

• Brianna N. Gaskill

  Novartis (United States)

  11 shared

• Jiangong Li

  10 shared

• Ryan N. Dilger

  University of Illinois Urbana-Champaign

  10 shared

• Betty R. McConn

  Oak Ridge Institute for Science and Education

  9 shared

• Brett C. Ramirez

  Iowa State University

  8 shared

Education

• Bachelor of Science, Biosystems and Agricultural Engineering

  University of Kentucky

• Master of Science, Biosystems and Agricultural Engineering

  University of Kentucky

• Doctor of Philosophy, Agricultural and Biosystems Engineering

  Iowa State University

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