Research Progress and Technology Trend of Intelligent Morning of Dairy Cow Motion Behavior
The motion behavior of dairy cows contains much of health information. The application of information and intelligent technology will help farms grasp the health status of dairy cows in time and improve breeding efficiency. In this paper, the development trend of intelligent morning technology of co...
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Format: | Article |
Language: | English |
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Editorial Office of Smart Agriculture
2022-06-01
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Series: | 智慧农业 |
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Online Access: | http://www.smartag.net.cn/article/2022/2096-8094/2096-8094-2022-4-2-36.shtml |
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author | WANG Zheng SONG Huaibo WANG Yunfei HUA Zhixin LI Rong XU Xingshi |
author_facet | WANG Zheng SONG Huaibo WANG Yunfei HUA Zhixin LI Rong XU Xingshi |
author_sort | WANG Zheng |
collection | DOAJ |
description | The motion behavior of dairy cows contains much of health information. The application of information and intelligent technology will help farms grasp the health status of dairy cows in time and improve breeding efficiency. In this paper, the development trend of intelligent morning technology of cow's motion behavior was mainly analyzed. Firstly, on the basis of expounding the significance of monitoring the basic motion (lying, walking, standing), oestrus, breathing, rumination and limping of dairy cows, the necessity of behavior monitoring of dairy cows was introduced. Secondly, the current research status was summarized from contact monitoring methods and non-contact monitoring methods in chronological order. The principle and achievements of related research were introduced in detail and classified. It is found that the current contact monitoring methods mainly rely on acceleration sensors, pedometers and pressure sensors, while the non-contact monitoring methods mainly rely on video images, including traditional video image analysis and video image analysis based on deep learning. Then, the development status of cow behavior monitoring industry was analyzed, and the main businesses and mainstream products of representative livestock farm automation equipment suppliers were listed. Industry giants, such as Afimilk and DeLaval, as well as their products such as intelligent collar (AfiCollar), pedometer (AfiActll Tag) and automatic milking equipment (VMS™ V300) were introduced. After that, the problems and challenges of current contact and non-contact monitoring methods of dairy cow motion behavior were put forward. The current intelligent monitoring methods of dairy cows' motion behavior are mainly wearable devices, but they have some disadvantages, such as bring stress to dairy cows and are difficult to install and maintain. Although the non-contact monitoring methods based on video image analysis technology does not bring stress to dairy cows and is low cost, the relevant research is still in its infancy , and there is still a certain distance from commercial use. Finally, the future development directions of relevant key technologies were prospected, including miniaturization and integration of wearable monitoring equipment, improving the robustness of computer vision technology, multi-target monitoring with limited equipment and promoting technology industrialization. |
first_indexed | 2024-12-10T17:28:28Z |
format | Article |
id | doaj.art-23923212f845449e8b6476bbfb84c048 |
institution | Directory Open Access Journal |
issn | 2096-8094 |
language | English |
last_indexed | 2024-12-10T17:28:28Z |
publishDate | 2022-06-01 |
publisher | Editorial Office of Smart Agriculture |
record_format | Article |
series | 智慧农业 |
spelling | doaj.art-23923212f845449e8b6476bbfb84c0482022-12-22T01:39:46ZengEditorial Office of Smart Agriculture智慧农业2096-80942022-06-0142365210.12133/j.smartag.SA202203011SA202203011Research Progress and Technology Trend of Intelligent Morning of Dairy Cow Motion BehaviorWANG Zheng0SONG Huaibo1WANG Yunfei2HUA Zhixin3LI Rong4XU Xingshi5College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, ChinaThe motion behavior of dairy cows contains much of health information. The application of information and intelligent technology will help farms grasp the health status of dairy cows in time and improve breeding efficiency. In this paper, the development trend of intelligent morning technology of cow's motion behavior was mainly analyzed. Firstly, on the basis of expounding the significance of monitoring the basic motion (lying, walking, standing), oestrus, breathing, rumination and limping of dairy cows, the necessity of behavior monitoring of dairy cows was introduced. Secondly, the current research status was summarized from contact monitoring methods and non-contact monitoring methods in chronological order. The principle and achievements of related research were introduced in detail and classified. It is found that the current contact monitoring methods mainly rely on acceleration sensors, pedometers and pressure sensors, while the non-contact monitoring methods mainly rely on video images, including traditional video image analysis and video image analysis based on deep learning. Then, the development status of cow behavior monitoring industry was analyzed, and the main businesses and mainstream products of representative livestock farm automation equipment suppliers were listed. Industry giants, such as Afimilk and DeLaval, as well as their products such as intelligent collar (AfiCollar), pedometer (AfiActll Tag) and automatic milking equipment (VMS™ V300) were introduced. After that, the problems and challenges of current contact and non-contact monitoring methods of dairy cow motion behavior were put forward. The current intelligent monitoring methods of dairy cows' motion behavior are mainly wearable devices, but they have some disadvantages, such as bring stress to dairy cows and are difficult to install and maintain. Although the non-contact monitoring methods based on video image analysis technology does not bring stress to dairy cows and is low cost, the relevant research is still in its infancy , and there is still a certain distance from commercial use. Finally, the future development directions of relevant key technologies were prospected, including miniaturization and integration of wearable monitoring equipment, improving the robustness of computer vision technology, multi-target monitoring with limited equipment and promoting technology industrialization.http://www.smartag.net.cn/article/2022/2096-8094/2096-8094-2022-4-2-36.shtmldairy cowsmotion behaviorhealth conditionintelligent monitoringsmart animal husbandry |
spellingShingle | WANG Zheng SONG Huaibo WANG Yunfei HUA Zhixin LI Rong XU Xingshi Research Progress and Technology Trend of Intelligent Morning of Dairy Cow Motion Behavior 智慧农业 dairy cows motion behavior health condition intelligent monitoring smart animal husbandry |
title | Research Progress and Technology Trend of Intelligent Morning of Dairy Cow Motion Behavior |
title_full | Research Progress and Technology Trend of Intelligent Morning of Dairy Cow Motion Behavior |
title_fullStr | Research Progress and Technology Trend of Intelligent Morning of Dairy Cow Motion Behavior |
title_full_unstemmed | Research Progress and Technology Trend of Intelligent Morning of Dairy Cow Motion Behavior |
title_short | Research Progress and Technology Trend of Intelligent Morning of Dairy Cow Motion Behavior |
title_sort | research progress and technology trend of intelligent morning of dairy cow motion behavior |
topic | dairy cows motion behavior health condition intelligent monitoring smart animal husbandry |
url | http://www.smartag.net.cn/article/2022/2096-8094/2096-8094-2022-4-2-36.shtml |
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