College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, China 712100; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi, China 712100; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Services, Yangling, China 712100; School of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China 310058.
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, China 712100; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi, China 712100; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Services, Yangling, China 712100.
J Dairy Sci. 2023 Apr;106(4):2963-2979. doi: 10.3168/jds.2022-22501. Epub 2023 Feb 14.
Automatic respiration monitoring of dairy cows in modern farming not only helps to reduce manual labor but also increases the automation of health assessment. It is common for cows to congregate on farms, which poses a challenge for manual observation of cow status because they physically occlude each other. In this study, we propose a method that can monitor the respiratory behavior of multiple cows. Initially, 4,000 manually labeled images were used to fine-tune the YOLACT (You Only Look At CoefficienTs) model for recognition and segmentation of multiple cows. Respiratory behavior in the resting state could better reflect their health status. Then, the specific resting states (lying resting, standing resting) of different cows were identified by fusing the convolutional neural network and bidirectional long and short-term memory algorithms. Finally, the corresponding detection algorithms (lying and standing resting) were used for respiratory behavior monitoring. The test results of 60 videos containing different interference factors indicated that the accuracy of respiratory behavior monitoring of multiple cows in 54 videos was >90.00%, and that of 4 videos was 100.00%. The average accuracy of the proposed method was 93.56%, and the mean absolute error and root mean square error were 3.42 and 3.74, respectively. Furthermore, the effectiveness of the method was analyzed for simultaneous monitoring of respiratory behavior of multiple cows under movement, occlusion disturbance, and behavioral changes. It was feasible to monitor the respiratory behavior of multiple cows based on the proposed algorithm. This study could provide an a priori technical basis for respiratory behavior monitoring and automatic diagnosis of respiratory-related diseases of multiple dairy cows based on biomedical engineering technology. In addition, it may stimulate researchers to develop robots with health-sensing functions that are oriented toward precision livestock farming.
现代化养殖中对奶牛的自动呼吸监测不仅有助于减少人工劳动，还提高了健康评估的自动化程度。奶牛通常会聚集在农场中，这给人工观察奶牛的状态带来了挑战，因为它们会彼此遮挡。在本研究中，我们提出了一种可以监测多只奶牛呼吸行为的方法。首先，我们使用了 4000 张手动标记的图像来微调 YOLACT（仅看系数）模型，以识别和分割多只奶牛。在休息状态下的呼吸行为可以更好地反映它们的健康状况。然后，通过融合卷积神经网络和双向长短时记忆算法，识别出不同奶牛的特定休息状态（卧息、立息）。最后，使用相应的检测算法（卧息和立息）对呼吸行为进行监测。对包含不同干扰因素的 60 个视频的测试结果表明，在 54 个视频中，多只奶牛呼吸行为监测的准确率>90.00%，在 4 个视频中准确率为 100.00%。所提出方法的平均准确率为 93.56%，平均绝对误差和均方根误差分别为 3.42 和 3.74。此外，还分析了该方法在运动、遮挡干扰和行为变化下同时监测多只奶牛呼吸行为的有效性。基于所提出的算法，对多只奶牛的呼吸行为进行监测是可行的。本研究可为基于生物医学工程技术的多只奶牛呼吸行为监测和与呼吸相关疾病的自动诊断提供先验技术基础。此外，它可能会激发研究人员开发具有健康感知功能的、面向精准畜牧养殖的机器人。
