Next Article in Journal
Trichoderma Biocontrol Performances against Baby-Lettuce Fusarium Wilt Surveyed by Hyperspectral Imaging-Based Machine Learning and Infrared Thermography
Previous Article in Journal
Efficacy and Phytotoxicity of Sulfur Dioxide Fumigation for Postharvest Control of Western Flower Thrips, Frankliniella occidentalis (Pergande) (Thysanoptera: Thripidae), on Select Fresh Fruit and Vegetables
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agriculture
Volume 14
Issue 2
10.3390/agriculture14020306
agriculture-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Computer Vision-Based Measurement Techniques for Livestock Body Dimension and Weight: A Review

by
Weihong Ma
1,†,
Xiangyu Qi
1,2,†,
Yi Sun
1,
Ronghua Gao
1,
Luyu Ding
1,
Rong Wang
1,
Cheng Peng
1,
Jun Zhang
1,
Jianwei Wu
1,
Zhankang Xu
1,
Mingyu Li
1,
Hongyan Zhao
3,
Shudong Huang
1,4,* and
Qifeng Li
1,*
1
Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
2
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
3
Otoke Banner Agricultural and Animal Husbandry Technology Extension Center, Ordos 016199, China
4
College of Computer Science, Sichuan University, Chengdu 610065, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agriculture 2024, 14(2), 306; https://doi.org/10.3390/agriculture14020306
Submission received: 14 December 2023 / Revised: 5 February 2024 / Accepted: 9 February 2024 / Published: 14 February 2024
(This article belongs to the Topic Precision Feeding and Management of Farm Animals, 2nd Edition)
Browse Figures
Review Reports Versions Notes

Abstract

Acquiring phenotypic data from livestock constitutes a crucial yet cumbersome phase in the breeding process. Traditionally, obtaining livestock phenotypic data primarily involves manual, on-body measurement methods. This approach not only requires extensive labor but also induces stress on animals, which leads to potential economic losses. Presently, the integration of next-generation Artificial Intelligence (AI), visual processing, intelligent sensing, multimodal fusion processing, and robotic technology is increasingly prevalent in livestock farming. The advantages of these technologies lie in their rapidity and efficiency, coupled with their capability to acquire livestock data in a non-contact manner. Based on this, we provide a comprehensive summary and analysis of the primary advanced technologies employed in the non-contact acquisition of livestock phenotypic data. This review focuses on visual and AI-related techniques, including 3D reconstruction technology, body dimension acquisition techniques, and live animal weight estimation. We introduce the development of livestock 3D reconstruction technology and compare the methods of obtaining 3D point cloud data of livestock through RGB cameras, laser scanning, and 3D cameras. Subsequently, we explore body size calculation methods and compare the advantages and disadvantages of RGB image calculation methods and 3D point cloud body size calculation methods. Furthermore, we also compare and analyze weight estimation methods of linear regression and neural networks. Finally, we discuss the challenges and future trends of non-contact livestock phenotypic data acquisition. Through emerging technologies like next-generation AI and computer vision, the acquisition, analysis, and management of livestock phenotypic data are poised for rapid advancement.
Keywords: 3D reconstruction; stressless body dimension measurement; visual weight estimation; precision livestock farming 3D reconstruction; stressless body dimension measurement; visual weight estimation; precision livestock farming

Share and Cite

MDPI and ACS Style

Ma, W.; Qi, X.; Sun, Y.; Gao, R.; Ding, L.; Wang, R.; Peng, C.; Zhang, J.; Wu, J.; Xu, Z.; et al. Computer Vision-Based Measurement Techniques for Livestock Body Dimension and Weight: A Review. Agriculture 2024, 14, 306. https://doi.org/10.3390/agriculture14020306

AMA Style

Ma W, Qi X, Sun Y, Gao R, Ding L, Wang R, Peng C, Zhang J, Wu J, Xu Z, et al. Computer Vision-Based Measurement Techniques for Livestock Body Dimension and Weight: A Review. Agriculture. 2024; 14(2):306. https://doi.org/10.3390/agriculture14020306

Chicago/Turabian Style

Ma, Weihong, Xiangyu Qi, Yi Sun, Ronghua Gao, Luyu Ding, Rong Wang, Cheng Peng, Jun Zhang, Jianwei Wu, Zhankang Xu, and et al. 2024. "Computer Vision-Based Measurement Techniques for Livestock Body Dimension and Weight: A Review" Agriculture 14, no. 2: 306. https://doi.org/10.3390/agriculture14020306

APA Style

Ma, W., Qi, X., Sun, Y., Gao, R., Ding, L., Wang, R., Peng, C., Zhang, J., Wu, J., Xu, Z., Li, M., Zhao, H., Huang, S., & Li, Q. (2024). Computer Vision-Based Measurement Techniques for Livestock Body Dimension and Weight: A Review. Agriculture, 14(2), 306. https://doi.org/10.3390/agriculture14020306

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop