Automatic identification and analysis of multi-object cattle rumination based on computer vision

Wang, Yueming; Chen, Tiantian; Li, Baoshan; Li, Qi

doi:10.5187/jast.2022.e87

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…The average accuracy and recall rate of this method in rumination behavior recognition were 95% and 98%, respectively. Wang et al used the frame difference method to recognize the dairy cow rumination behavior [47]. To verify the feasibility of this method, the algorithm was tested on multi-object dairy cow rumination videos, the rumination time and chewing frequency were calculated, then a comparison was made with the results of manual observation.…”

Section: Application Of Machine Vision In Main Behavior Recognition O...mentioning

confidence: 99%

Progress of Machine Vision Technologies in Intelligent Dairy Farming

et al. 2023

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The large-scale and precise intelligent breeding mode for dairy cows is the main direction for the development of the dairy industry. Machine vision has become an important technological means for the intelligent breeding of dairy cows due to its non-invasive, low-cost, and multi-behavior recognition capabilities. This review summarizes the recent application of machine vision technology, machine learning, and deep learning in the main behavior recognition of dairy cows. The authors summarized identity recognition technology based on facial features, muzzle prints, and body features of dairy cows; motion behavior recognition technology such as lying, standing, walking, drinking, eating, rumination, estrus; and the recognition of common diseases such as lameness and mastitis. Based on current research results, machine vision technology will become one of the important technological means for the intelligent breeding of dairy cows. Finally, the author also summarized the advantages of this technology in intelligent dairy farming, as well as the problems and challenges faced in the next development.

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Section: Application Of Machine Vision In Main Behavior Recognition O...mentioning

confidence: 99%

Progress of Machine Vision Technologies in Intelligent Dairy Farming

et al. 2023

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“…Ayadi et al [ 20 ] used a deep learning model based on convolutional neural networks (CNN) to identify rumination behavior by monitoring all cow postures captured by cameras, achieving an accuracy rate of 98%. Wang et al [ 21 ] employed a combination of the YOLO algorithm and the kernelized correlation filter (KCF) to count the rumination chews of cattle. The results indicated an average error rate of 8.126%.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Cattle Ruminating Behavior Based on an Improved Keypoint Detection Model

Li,

Liu,

Zheng

et al. 2024

Animals

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Cattle rumination behavior is strongly correlated with its health. Current methods often rely on manual observation or wearable devices to monitor ruminating behavior. However, the manual monitoring of cattle rumination is labor-intensive, and wearable devices often harm animals. Therefore, this study proposes a non-contact method for monitoring cattle rumination behavior, utilizing an improved YOLOv8-pose keypoint detection algorithm combined with multi-condition threshold peak detection to automatically identify chewing counts. First, we tracked and recorded the cattle’s rumination behavior to build a dataset. Next, we used the improved model to capture keypoint information on the cattle. By constructing the rumination motion curve from the keypoint information and applying multi-condition threshold peak detection, we counted the chewing instances. Finally, we designed a comprehensive cattle rumination detection framework to track various rumination indicators, including chewing counts, rumination duration, and chewing frequency. In keypoint detection, our modified YOLOv8-pose achieved a 96% mAP, an improvement of 2.8%, with precision and recall increasing by 4.5% and 4.2%, enabling the more accurate capture of keypoint information. For rumination analysis, we tested ten video clips and compared the results with actual data. The experimental results showed an average chewing count error of 5.6% and a standard error of 2.23%, verifying the feasibility and effectiveness of using keypoint detection technology to analyze cattle rumination behavior. These physiological indicators of rumination behavior allow for the quicker detection of abnormalities in cattle’s rumination activities, helping managers make informed decisions. Ultimately, the proposed method not only accurately monitors cattle rumination behavior but also provides technical support for precision management in animal husbandry, promoting the development of modern livestock farming.

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