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基于計(jì)算機(jī)視覺的奶牛體況評(píng)分研究綜述
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國家自然科學(xué)基金項(xiàng)目(42071449,、41601491)


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

    目前奶牛體況評(píng)分主要為人工,,但受人工主觀性影響,評(píng)分結(jié)果的可靠性較差,,評(píng)定過程耗時(shí)費(fèi)力,,嚴(yán)重依賴于評(píng)估人員的經(jīng)驗(yàn),基于計(jì)算機(jī)視覺的奶牛體況評(píng)分研究逐漸成為研究熱點(diǎn),。奶牛體況評(píng)分的發(fā)展主要經(jīng)歷了人工評(píng)分階段,、傳統(tǒng)機(jī)器學(xué)習(xí)階段和深度學(xué)習(xí)階段,后兩者又可細(xì)分為2D領(lǐng)域和3D領(lǐng)域的研究,。當(dāng)前基于傳統(tǒng)機(jī)器學(xué)習(xí)的奶牛體況評(píng)分方法主要存在依賴于人工標(biāo)記的問題,,單純地改進(jìn)降維、提取特征的方法,,只能在特定的情況得到提高,,使用場景局限,且效果提升有限。隨著深度學(xué)習(xí)的興起,,研究者們開始對不需要人工標(biāo)記特征的方法進(jìn)行探索,。深度學(xué)習(xí)與3D技術(shù)的使用使得自動(dòng)體況評(píng)分的精度有了進(jìn)一步的提升,但在實(shí)際生產(chǎn)中,,為滿足奶牛不同生長階段營養(yǎng)管理需求,,奶牛體況值與理想值差應(yīng)始終維持在±0.25以內(nèi),現(xiàn)有自動(dòng)評(píng)分系統(tǒng)的精度與實(shí)際養(yǎng)殖管理的理想標(biāo)準(zhǔn)仍具有一定差距,。本文通過文獻(xiàn)分析,,對當(dāng)前利用計(jì)算機(jī)視覺的奶牛體況評(píng)分的研究熱點(diǎn)和理論進(jìn)行總結(jié)研究,提出潛在的研究方向,。

    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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吳宇峰,李一鳴,趙遠(yuǎn)洋,楊 普,李振波,郭 浩.基于計(jì)算機(jī)視覺的奶牛體況評(píng)分研究綜述[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),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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  • 在線發(fā)布日期: 2021-11-10
  • 出版日期: 2021-12-10
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