基于计算机视觉的奶牛体况评分研究综述
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国家自然科学基金项目(42071449、41601491)


Review of Research on Body Condition Score for Dairy Cows Based on Computer Vision
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    摘要:

    目前奶牛体况评分主要为人工,但受人工主观性影响,评分结果的可靠性较差,评定过程耗时费力,严重依赖于评估人员的经验,基于计算机视觉的奶牛体况评分研究逐渐成为研究热点。奶牛体况评分的发展主要经历了人工评分阶段、传统机器学习阶段和深度学习阶段,后两者又可细分为2D领域和3D领域的研究。当前基于传统机器学习的奶牛体况评分方法主要存在依赖于人工标记的问题,单纯地改进降维、提取特征的方法,只能在特定的情况得到提高,使用场景局限,且效果提升有限。随着深度学习的兴起,研究者们开始对不需要人工标记特征的方法进行探索。深度学习与3D技术的使用使得自动体况评分的精度有了进一步的提升,但在实际生产中,为满足奶牛不同生长阶段营养管理需求,奶牛体况值与理想值差应始终维持在±0.25以内,现有自动评分系统的精度与实际养殖管理的理想标准仍具有一定差距。本文通过文献分析,对当前利用计算机视觉的奶牛体况评分的研究热点和理论进行总结研究,提出潜在的研究方向。

    Abstract:

    At present, body condition score for dairy cows mainly adopts manual methods, but the reliability of the scoring results is poor due to manual subjectivity, and the assessment process is time-consuming and laborious, which relies heavily on the experience of experts. The development of body condition score for dairy cows has mainly gone through manual scoring stage, traditional machine learning stage and deep learning stage, the latter two can be subdivided into 2D field and 3D field research. Body condition score method for dairy cows based on machine learning mainly suffers from the problem of relying on manual markers and simply improving the method of dimensionality reduction and feature extraction, which can only be improved in specific situations, with limited improvement in results. With the rise of deep learning, researchers have begun to explore methods that do not require manually labeled features. The use of deep learning and 3D technology has further improved the accuracy of automatic body condition scoring, but in actual production, to meet the nutritional management needs of cows at different growth stages, the difference between the body condition score and the ideal score should always be maintained within ±0.25, and the accuracy of existing automatic scoring systems still has a certain gap with the ideal standard of actual farm management. The current research hotspots and theories of body condition score methods were summarized for dairy cows using computer vision by analyzing the literature and potential research directions were proposed. With the development of artificial intelligence, a large number of deep learning algorithms emerged that can be used for target detection and classification. These methods were also applicable to target detection and classification in the field of animal husbandry. In fact, artificial intelligence and deep learning techniques were increasingly being used in the livestock sector as well. Deep learning methods were needed for dairy cattle condition scoring, and as the development of agricultural information technology became more mature, research on automated body condition score methods for dairy cows based on deep learning would also become more advanced.

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吴宇峰,李一鸣,赵远洋,杨 普,李振波,郭 浩.基于计算机视觉的奶牛体况评分研究综述[J].农业机械学报,2021,52(S0):268-275. WU Yufeng, LI Yiming, ZHAO Yuanyang, YANG Pu, LI Zhenbo, GUO Hao. Review of Research on Body Condition Score for Dairy Cows Based on Computer Vision[J]. Transactions of the Chinese Society for Agricultural Machinery,2021,52(S0):268-275.

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  • 收稿日期:2021-07-09
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  • 在线发布日期: 2021-11-10
  • 出版日期: 2021-12-10
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