Technical note: ShinyAnimalCV: open-source cloud-based web application for object detection, segmentation, and three-dimensional visualization of animals using computer vision.
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| Title: | Technical note: ShinyAnimalCV: open-source cloud-based web application for object detection, segmentation, and three-dimensional visualization of animals using computer vision. |
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| Authors: | Wang, Jin1 (AUTHOR), Hu, Yu1 (AUTHOR), Xiang, Lirong2 (AUTHOR), Morota, Gota3 (AUTHOR), Brooks, Samantha A1 (AUTHOR), Wickens, Carissa L1 (AUTHOR), Miller-Cushon, Emily K1 (AUTHOR), Yu, Haipeng1 (AUTHOR) |
| Source: | Journal of Animal Science. 2024, Vol. 102, p1-6. 6p. |
| Document Type: | Article |
| Subjects: | Science education, Object recognition (Computer vision), Three-dimensional imaging, Machine learning, Animal communities, Deep learning, Computer vision |
| Author-Supplied Keywords: | computer vision morphological features object detection object segmentation shiny application three-dimensional visualization |
| Abstract: | Computer vision (CV), a non-intrusive and cost-effective technology, has furthered the development of precision livestock farming by enabling optimized decision-making through timely and individualized animal care. The availability of affordable two- and three-dimensional camera sensors, combined with various machine learning and deep learning algorithms, has provided a valuable opportunity to improve livestock production systems. However, despite the availability of various CV tools in the public domain, applying these tools to animal data can be challenging, often requiring users to have programming and data analysis skills, as well as access to computing resources. Moreover, the rapid expansion of precision livestock farming is creating a growing need to educate and train animal science students in CV. This presents educators with the challenge of efficiently demonstrating the complex algorithms involved in CV. Thus, the objective of this study was to develop ShinyAnimalCV, an open-source cloud-based web application designed to facilitate CV teaching in animal science. This application provides a user-friendly interface for performing CV tasks, including object segmentation, detection, three-dimensional surface visualization, and extraction of two- and three-dimensional morphological features. Nine pre-trained CV models using top-view animal data are included in the application. ShinyAnimalCV has been deployed online using cloud computing platforms. The source code of ShinyAnimalCV is available on GitHub, along with detailed documentation on training CV models using custom data and deploying ShinyAnimalCV locally to allow users to fully leverage the capabilities of the application. ShinyAnimalCV can help to support the teaching of CV, thereby laying the groundwork to promote the adoption of CV in the animal science community. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Animal Science is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Author Affiliations: | 1Department of Animal Sciences, University of Florida, Gainesville, FL 32611, USA 2Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC 27695, USA 3School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA |
| Full Text Word Count: | 4255 |
| ISSN: | 0021-8812 |
| DOI: | 10.1093/jas/skad416 |
| Accession Number: | 182431564 |
| Database: | Veterinary Source |
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| Abstract: | Computer vision (CV), a non-intrusive and cost-effective technology, has furthered the development of precision livestock farming by enabling optimized decision-making through timely and individualized animal care. The availability of affordable two- and three-dimensional camera sensors, combined with various machine learning and deep learning algorithms, has provided a valuable opportunity to improve livestock production systems. However, despite the availability of various CV tools in the public domain, applying these tools to animal data can be challenging, often requiring users to have programming and data analysis skills, as well as access to computing resources. Moreover, the rapid expansion of precision livestock farming is creating a growing need to educate and train animal science students in CV. This presents educators with the challenge of efficiently demonstrating the complex algorithms involved in CV. Thus, the objective of this study was to develop ShinyAnimalCV, an open-source cloud-based web application designed to facilitate CV teaching in animal science. This application provides a user-friendly interface for performing CV tasks, including object segmentation, detection, three-dimensional surface visualization, and extraction of two- and three-dimensional morphological features. Nine pre-trained CV models using top-view animal data are included in the application. ShinyAnimalCV has been deployed online using cloud computing platforms. The source code of ShinyAnimalCV is available on GitHub, along with detailed documentation on training CV models using custom data and deploying ShinyAnimalCV locally to allow users to fully leverage the capabilities of the application. ShinyAnimalCV can help to support the teaching of CV, thereby laying the groundwork to promote the adoption of CV in the animal science community. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00218812 |
| DOI: | 10.1093/jas/skad416 |