Field Implementation of Precision Livestock Farming: Selected Proceedings from the 2nd U.S. Precision Livestock Farming Conference-Reference-Cited by-同舟云学术

Field Implementation of Precision Livestock Farming: Selected Proceedings from the 2nd U.S. Precision Livestock Farming Conference

Published:2024-04-07 Issue:7 Volume:14 Page:1128
ISSN:2076-2615
Container-title:Animals
language:en
Short-container-title:Animals

Author:

Zhao Yang¹^ORCID,Ramirez Brett C.²^ORCID,Siegford Janice M.³^ORCID,Gan Hao⁴^ORCID,Wang-Li Lingjuan⁵^ORCID,Berckmans Daniel⁴,Burns Robert T.⁴^ORCID

Affiliation:

1. Department of Animal Science, The University of Tennessee, Knoxville, TN 37996, USA

2. Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50011, USA

3. Department of Animal Science, Michigan State University, East Lansing, MI 48824, USA

4. Department of Biosystems Engineering and Soil Science, The University of Tennessee, Knoxville, TN 37996, USA

5. Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC 27695, USA

Abstract

Precision Livestock Farming (PLF) involves the real-time monitoring of images, sounds, and other biological, physiological, and environmental parameters to assess and improve animal health and welfare within intensive and extensive production systems [...]

Funder

United States Department of Agriculture (USDA) National Institute of Food and Agriculture

University of Tennessee Institute of Agriculture AgResearch, and industry partners

Publisher

MDPI AG

Link

https://www.mdpi.com/2076-2615/14/7/1128/pdf

Reference16 articles.

1. Menendez, H.M., Brennan, J.R., Ehlert, K.A., and Parsons, I.L. (2023). Improving Dry Matter Intake Estimates Using Precision Body Weight on Cattle Grazed on Extensive Rangelands. Animals, 13.

2. Van Steenkiste, G., Van Den Brulle, I., Piepers, S., and De Vliegher, S. (2023). In-Line Detection of Clinical Mastitis by Identifying Clots in Milk Using Images and a Neural Network Approach. Animals, 13.

3. Islam, M.N., Yoder, J., Nasiri, A., Burns, R.T., and Gan, H. (2023). Analysis of the Drinking Behavior of Beef Cattle Using Computer Vision. Animals, 13.

4. Jacobs, J.L., Hersom, M.J., Andrae, J.G., and Duckett, S.K. (2023). Training and Adaptation of Beef Calves to Precision Supplementation Technology for Individual Supplementation in Grazing Systems. Animals, 13.

5. Nyamuryekung’e, S., Duff, G., Utsumi, S., Estell, R., McIntosh, M.M., Funk, M., Cox, A., Cao, H., Spiegal, S., and Perea, A. (2023). Real-Time Monitoring of Grazing Cattle Using LORA-WAN Sensors to Improve Precision in Detecting Animal Welfare Implications via Daily Distance Walked Metrics. Animals, 13.