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計(jì)算機(jī)視覺技術(shù)在家禽養(yǎng)殖與公雞選種中應(yīng)用綜述
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國(guó)家重大科技基礎(chǔ)建設(shè)項(xiàng)目(4444-1009609)


Computer Vision in Poultry Breeding and Rooster Selection
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    摘要:

    我國(guó)家禽養(yǎng)殖規(guī)模和數(shù)量在不斷擴(kuò)大,智能化養(yǎng)殖是當(dāng)前家禽飼養(yǎng)監(jiān)管的熱點(diǎn)研究方向。計(jì)算機(jī)視覺技術(shù)能夠提供無創(chuàng)、非侵入式、低成本、高效益的動(dòng)物行為識(shí)別方式,用于檢測(cè)雞群活動(dòng)水平、診斷疾病和發(fā)現(xiàn)死禽??偨Y(jié)了用于檢測(cè)雞和識(shí)別雞行為的視覺系統(tǒng),并從表型參數(shù)和行為參數(shù)兩方面分別回顧了與繁殖性能的相關(guān)關(guān)系、表型性狀特征提取與識(shí)別以及行為識(shí)別算法;分析了當(dāng)前視覺系統(tǒng)存在的問題并提出其優(yōu)化策略;討論了基于計(jì)算機(jī)視覺技術(shù)選留優(yōu)質(zhì)種公雞的可行性,并初步提出了公雞選種算法框架。最后展望了計(jì)算機(jī)視覺技術(shù)在家禽養(yǎng)殖行業(yè)的應(yīng)用前景及優(yōu)化方向。

    Abstract:

    With Chinese poultry production scale and quantity expanding, intelligent breeding is the current hot research direction of the regulation of poultry. Computer vision technology can be used to provide a noninvasive, non-invasive, low-cost and highly effective way to identify animals behaviors to detect the activity level, diagnose the diseases of chickens and find the dead ones. The visual systems of chicken detection and behavior recognition were summarized, and the correlativity between the phenotypic parameters, behavior parameters and reproductivity respectively, phenotypic feature extraction and recognition and behavior recognition algorithm were reviewed; the problems in visual system were analyzed, the optimization strategy was put forward; the feasibility of using computer vision technique to select high quality breeder rooster was discussed, and the rooster selection algorithm framework was preliminarily proposed. Finally, the applied prospects and the optimization directions of computer vision technology in the poultry industry were expected.

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孫一平,李 丹,藺旭鴻,陳一飛.計(jì)算機(jī)視覺技術(shù)在家禽養(yǎng)殖與公雞選種中應(yīng)用綜述[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2021,52(S0):219-228,283. SUN Yiping, LI Dan, LIN Xunhong, CHEN Yifei. Computer Vision in Poultry Breeding and Rooster Selection[J]. Transactions of the Chinese Society for Agricultural Machinery,2021,52(S0):219-228,283.

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  • 收稿日期:2021-07-15
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  • 在線發(fā)布日期: 2021-11-10
  • 出版日期: 2021-12-10