基于计算机视觉的奶牛生理参数监测与疾病诊断研究进展及挑战

Advances and Challenges in Physiological Parameters Monitoring and Diseases Diagnosing of Dairy Cows Based on Computer Vision

利用先进的信息技术推动智能养殖业发展已经成为奶牛养殖研究领域的重要目标和任务。计算机视觉技术具有非接触、免应激、低成本及高通量等优点,在畜牧生产中应用前景广阔。本文在阐述了计算机视觉技术在智能化养殖业发展中重要性的基础上,首先介绍了基于计算机视觉的奶牛生理参数监测进展,包括体尺、体温、体重的前沿监测设备、技术和模型参数。然后阐述了奶牛跛行及乳腺炎等疾病诊断的前沿技术发展过程和研究现状。目前,相关技术研究和应用推广存在检测准确性不高,受环境因素影响较大,非标准化养殖场结构制约检测系统普及,以及检测系统成本较高等问题和挑战。最后,本文结合中国养殖业发展现状,针对保证检测准确性、减少环境干扰等问题,就如何提高计算机视觉技术在智能化养殖业中的准确性和普适性提出了相关建议,旨在为中国奶牛养殖业的科学管理和现代化生产提供新方法和新思路。

Realizing the construction of intelligent farming by using advanced information technology,thus improving the living welfare of dairy cows and the economic benefits of dairy farms has become an important goal and task in dairy farming research field.Computer vision technology has the advantages of non-contact,stress-free,low cost and high throughput,and has a broad application prospect in animal production.On the basis of describing the importance of computer vision technology in the development of intelligent farming industry,this paper introduced the cutting-edge technology of cow physiological parameters and disease diagnosis based on computer vision,including cow temperature monitoring,body size monitoring,weight measurement,mastitis detection and lameness detection.The introduction coverd the development process of these studies,the current mainstream techniques,and discussed the problems and challenges in the research and application of related technology,aiming at the problem that the current computer vision-based detection methods are susceptible to individual difference and environmental changes.Combined with the development status of farming industry,suggestions on how to improve the universality of computer vision technology in intelligent farming industry,how to improve the accuracy of monitoring cows’ physiological parameters and disease diagnosis,and how to reduce the influence of environment on the system were put forward.Future research work should focus on research and developmentof algorithm,make full use of computer vision technology continuous detection and the advantage of large amount of data,to ensure the accuracy of the detection,and improve the function of the system integration and data utilization,expand the computer vision system function.Under the premise that does not affect the ability of the system,to improve the study on the number of function integration and system function and reduce equipment costs.