Cow behavior recognition based on image analysis and activities

• Published in: International journal of agricultural and biological engineering Vol. 10; no. 3; p. 165
• Main Authors: Jingqiu, Gu, Zhihai, Wang, Ronghua, Gao, Huarui, Wu
• Format: Journal Article
• Language: English
• Published: Beijing International Journal of Agricultural and Biological Engineering (IJABE) 01.05.2017
• Subjects: Accuracy; Animal behavior; Breeding of animals; Classification; Dairy cattle; Dairy farming; Efficiency; Entropy; Farming; Heat; Hoof; Identification; Image analysis; Image processing; Intensive farming; Internet of Things; Mapping; Milk; Movement; Neural networks; Object recognition; Pattern recognition; Real time; Reproduction; Surveillance; Target detection; Target recognition
• ISSN: 1934-6344; 1934-6352
• DOI: 10.3965/j.ijabe.20171003.3080

For the rapid and accurate identification of cow reproduction and healthy behavior from mass surveillance video, in this study, 400 head of young cows and lactating cows were taken as the research object and analyzed cow behavior from the dairy activity area and milk hall ramp. The method of object recognition based on image entropy was proposed, aiming at the identification of motional cow object behavior against a complex background. Calculating a minimum bounding box and contour mapping were used for the real-time capture of rutting span behavior and hoof or back characteristics. Then, by combining the continuous image characteristics and movement of cows for 7 d, the method could quickly distinguish abnormal behavior of dairy cows from healthy reproduction, improving the accuracy of the identification of characteristics of dairy cows. Cow behavior recognition based on image analysis and activities was proposed to capture abnormal behavior that has harmful effects on healthy reproduction and to improve the accuracy of cow behavior identification. The experimental results showed that, through target detection, classification and recognition, the recognition rates of hoof disease and heat in the reproduction and health of dairy cows were greater than 80%, and the false negative rates of oestrus and hoof disease were 3.28% and 5.32%, respectively. This method can enhance the real-time monitoring of cows, save time and improve the management efficiency of large-scale farming.