• 《中国科学引文数据库(CSCD)》来源期刊
  • 中国科技期刊引证报告(核心版)期刊
  • 《中文核心期刊要目总览》核心期刊
  • RCCSE中国核心学术期刊

肉牛智慧养殖技术研究进展

罗西尔, 卢小龙, 刘庆友, 崔奎青

罗西尔, 卢小龙, 刘庆友, 等. 肉牛智慧养殖技术研究进展[J]. 华南农业大学学报, 2024, 45(5): 661-671. DOI: 10.7671/j.issn.1001-411X.202405032
引用本文: 罗西尔, 卢小龙, 刘庆友, 等. 肉牛智慧养殖技术研究进展[J]. 华南农业大学学报, 2024, 45(5): 661-671. DOI: 10.7671/j.issn.1001-411X.202405032
LUO Xi’er, LU Xiaolong, LIU Qingyou, et al. Research progress on intelligent farming techniques of beef cattle[J]. Journal of South China Agricultural University, 2024, 45(5): 661-671. DOI: 10.7671/j.issn.1001-411X.202405032
Citation: LUO Xi’er, LU Xiaolong, LIU Qingyou, et al. Research progress on intelligent farming techniques of beef cattle[J]. Journal of South China Agricultural University, 2024, 45(5): 661-671. DOI: 10.7671/j.issn.1001-411X.202405032

肉牛智慧养殖技术研究进展

基金项目: 国家重点研发计划(2023YFD2000700);广东省季华实验室基金项目(X220991UZ230)
详细信息
    作者简介:

    罗西尔,讲师,博士,主要从事反刍动物优质性状基因的研究及智慧生产繁育系统的开发,E-mail: luoxier@fosu.edu.cn

    通讯作者:

    崔奎青,教授,博士,主要从事畜禽智慧养殖与动物生殖生理研究,E-mail: kqcui@fosu.edu.cn

  • 中图分类号: S818.9;S823

Research progress on intelligent farming techniques of beef cattle

  • 摘要:

    肉牛智慧养殖技术是肉牛养殖业由粗放型向集约型转型升级的关键技术,在提升养殖效益及管理效率上发挥越来越重要的作用。国内肉牛养殖业面临着智能化设备利用率低、养殖场管理效率低、养殖成本偏高等产业突出问题。本文从肉牛个体识别技术、智慧表型采集技术、智慧发情鉴定技术、自动化饲喂技术、疾病检测技术、以及牛舍环境监测与清洁等6个方面概述了当前肉牛智慧养殖技术的研究进展和现状,阐述了关键技术的应用和原理,并对今后肉牛智慧养殖技术的发展进行了展望,以期为我国肉牛养殖智慧化发展提供参考。

    Abstract:

    Beef cattle intelligent farming technology is the key technology for the transformation and upgrading of beef cattle farming from extensive to intensive, and it plays an increasingly important role in enhancing farming efficiency and management efficiency. Domestic beef cattle farming industry faces outstanding problems such as low utilization rate of intelligent equipment, low farm management efficiency and high farming cost. This paper outlines the current research progress and status of beef cattle intelligent farming technology from six aspects, including beef cattle individual identification technology, intelligent phenotype collection technology, intelligent estrus identification technology, automated feeding technology, disease detection technology, as well as environmental monitoring and cleaning of the barn, etc., describes the application of the key technologies and the principles, and looks forward to the future development of beef cattle intelligent farming technology, with a view to providing a reference for China’s beef cattle aquaculture intelligent development.

  • 图  1   牛视网膜、虹膜、口鼻纹识别所用的生物特征

    A:视网膜血管分支[7],B:虹膜[8],C:口鼻纹[9]

    Figure  1.   Biometrics for bovine retinal, iris and muzzle recognition

    A: Retinal branch, B: Iris, C: Muzzle

    图  2   不同姿态肉牛体型测量值的差异[22]

    Figure  2.   The differences of body size measurements of beef cattle with different postures

    图  3   CowXNet系统对身体部位的预测效果[29]

    绿色表示人工标签,红色表示预测点;红色点与绿色点基本重合表明预测效果良好

    Figure  3.   Predictive effects of CowXNet system on body parts

    Green represents artificial labels, red represents prediction points; The fact that the red and green points basically coincide indicates that the prediction is good

    图  4   病变后蹄(A)和非病变后蹄(B)的红外热成像对比图[48]

    Figure  4.   Comparison of infrared thermography of diseased hind hooves (A) and non-diseased hind hooves (B)

    表  1   非接触式个体识别技术准确率对比

    Table  1   Accuracy comparison of non-contact individual recognition technology

    类别
    Category
    主要识别方法
    Main identification method
    准确率/%
    Accuracy
    参考文献
    Reference
    口鼻纹
    Snout print
    特征提取和匹配算法
    MuzzleView
    98.85 [9]
    口鼻纹
    Snout print
    特征提取算法
    Local binary pattern
    99.50 [6]
    视网膜
    Retina
    人工观察 96.20 [13]
    虹膜
    Iris
    基于复小波变换的
    图像处理技术
    98.33 [8]
    躯干
    Torso
    计算机视觉
    TLAINS-InceptionV3
    99.74 [7]
    面部
    Face
    面部
    Face
    面部
    Face
    计算机视觉
    VGG-Face+Keras
    98.60 [8]
    计算机视觉
    VGG16
    91.90 [13]
    计算机视觉
    MobileNetV1+K-means++
    99.86 [12]
    下载: 导出CSV

    表  2   智慧发情鉴定技术识别准确率对比

    Table  2   The comparison of recognition accuracy of intelligent estrus detection technology

    类别
    Category
    主要识别方法
    Main identification method
    准确率/%
    Accuracy
    参考文献
    Reference
    阴道内温度 Vaginal temperature 温度传感器 96.00 [24, 33]
    爬跨 Climbing span 压力传感器+自动摄像系统 <77.00 [24]
    爬跨 Climbing span 基于无线电遥测的压力传感系统 77.00 [34]
    运动量 Physical activity 耳标内的3D加速度计 97.00 [25]
    运动量 Physical activity 安装在腿部或颈部的3D加速度计 92.00 [35]
    爬跨 Climbing span 计算机视觉 AlexNet 88.24 [27]
    爬跨 Climbing span 计算机视觉 DenseBlock+YOLO v3 97.62 [28]
    爬跨 Climbing span 计算机视觉 CowXNet 83.00 [29]
    爬跨 Climbing span 计算机视觉 CNN+VGG-19 95.00 [30]
    孕酮 Progesterone 奶样孕酮分析仪 94.00 [36]
    下载: 导出CSV
  • [1]

    GREENWOOD P L. Review: An overview of beef production from pasture and feedlot globally, as demand for beef and the need for sustainable practices increase[J]. Animal, 2021, 15: 100295. doi: 10.1016/j.animal.2021.100295

    [2] 宋一凡, 王娟, 李建丽, 等. 精准化养殖模式下牛只个体识别方法综述[J]. 黑龙江畜牧兽医, 2021(22): 48-53.
    [3]

    AWAD A I. From classical methods to animal biometrics: A review on cattle identification and tracking[J]. Computers and Electronics in Agriculture, 2016, 123: 423-435. doi: 10.1016/j.compag.2016.03.014

    [4]

    DUROC Y, TEDJINI S. RFID: A key technology for Humanity[J]. Comptes Rendus Physique, 2018, 19(1/2): 64-71.

    [5]

    BARANOV A S, GRAML R, PIRCHNER F, et al. Breed differences and intra-breed genetic variability of dermatoglyphic pattern of cattle[J]. Journal of Animal Breeding and Genetics, 1993, 110(1/2/3/4/5/6): 385-392.

    [6] 王斌. 牛个体面部识别算法研究与设计[D]. 呼和浩特: 内蒙古大学, 2022.
    [7] 张宇. 基于深度学习的肉牛体侧识别方法研究[D]. 包头: 内蒙古科技大学, 2023.
    [8] 刘爽. 基于深度学习的西门塔尔肉牛面部识别的研究[D]. 呼和浩特: 内蒙古大学, 2020.
    [9]

    BARRY B, GONZALES-BARRON U A, MCDONNELL K, et al. Using muzzle pattern recognition as a biometric approach for cattle identification[J]. Transactions of the ASABE, 2007, 50(3): 1073-1080. doi: 10.13031/2013.23121

    [10]

    KIM H T, CHOI H L, LEE D W, et al. Recognition of individual Holstein cattle by imaging body patterns[J]. Asian-Australasian Journal of Animal Sciences, 2005, 18(8): 1194-1198. doi: 10.5713/ajas.2005.1194

    [11] 赵凯旋, 何东健. 基于卷积神经网络的奶牛个体身份识别方法[J]. 农业工程学报, 2015, 31(5): 181-187. doi: 10.3969/j.issn.1002-6819.2015.05.026
    [12] 李征. 局部遮挡条件下的西门塔尔肉牛面部识别算法研究[D]. 呼和浩特: 内蒙古大学, 2022.
    [13] 张晨鹏. 基于深度学习的牛脸检测与个体身份识别方法研究[D]. 呼和浩特: 内蒙古工业大学, 2021.
    [14]

    WANGCHUK K, WANGDI J, MINDU M D. Comparison and reliability of techniques to estimate live cattle body weight[J]. Journal of Applied Animal Research, 2018, 46(1): 349-352. doi: 10.1080/09712119.2017.1302876

    [15]

    FIRDAUS F, ATMOKO B A, BALIARTI E, et al. The meta-analysis of beef cattle body weight prediction using body measurement approach with breed, sex, and age categories[J]. Journal of Advanced Veterinary and Animal Research, 2023, 10(4): 630.

    [16]

    BROWN D J, SAVAGE D B, HINCH G N, et al. Monitoring liveweight in sheep is a valuable management strategy: A review of available technologies[J]. Animal Production Science, 2015, 55(4): 427-436. doi: 10.1071/AN13274

    [17]

    GONZÁLEZ-GARCÍA E, ALHAMADA M, PRADEL J, et al. A mobile and automated walk-over-weighing system for a close and remote monitoring of liveweight in sheep[J]. Computers and Electronics in Agriculture, 2018, 153: 226-238. doi: 10.1016/j.compag.2018.08.022

    [18]

    ALDRIDGE M N, LEE S J, TAYLOR J D, et al. The use of walk over weigh to predict calving date in extensively managed beef herds[J]. Animal Production Science, 2016, 57(3): 583-591.

    [19]

    PARSONS I L, NORMAN D A, KARISCH B B, et al. Automated walk-over-weigh system to track daily body mass and growth in grazing steers[J]. Computers and Electronics in Agriculture, 2023, 212: 108113. doi: 10.1016/j.compag.2023.108113

    [20]

    GRITSENKO S, RUCHAY A, KOLPAKOV V, et al. On-barn forecasting beef cattle production based on automated non-contact body measurement system[J]. Animals, 2023, 13(4): 611. doi: 10.3390/ani13040611

    [21]

    LI J W, LI Q F, MA W H, et al. Key region extraction and body dimension measurement of beef cattle using 3D point clouds[J]. Agriculture, 2022, 12(7): 1012. doi: 10.3390/agriculture12071012

    [22]

    LI J W, MA W H, BAI Q, et al. A posture-based measurement adjustment method for improving the accuracy of beef cattle body size measurement based on point cloud data[J]. Biosystems Engineering, 2023, 230: 171-190. doi: 10.1016/j.biosystemseng.2023.04.014

    [23]

    SCHORI F, MUENGER A. Assessment of two wireless reticulo-rumen pH sensors for dairy cows[J]. Agrarforschung Schweiz, 2022, 13(2): 11-16.

    [24]

    SAINT-DIZIER M, CHASTANT-MAILLARD S. Potential of connected devices to optimize cattle reproduction[J]. Theriogenology, 2018, 112: 53-62. doi: 10.1016/j.theriogenology.2017.09.033

    [25]

    SCHWEINZER V, GUSTERER E, KANZ P, et al. Evaluation of an ear-attached accelerometer for detecting estrus events in indoor housed dairy cows[J]. Theriogenology, 2019, 130: 19-25. doi: 10.1016/j.theriogenology.2019.02.038

    [26]

    TSAI D M, HUANG C Y. A motion and image analysis method for automatic detection of estrus and mating behavior in cattle[J]. Computers and Electronics in Agriculture, 2014, 104: 25-31. doi: 10.1016/j.compag.2014.03.003

    [27] 王少华, 何东健, 刘冬. 基于机器视觉的奶牛发情行为自动识别方法[J]. 农业机械学报, 2020, 51(4): 241-249. doi: 10.6041/j.issn.1000-1298.2020.04.028
    [28] 王少华, 何东健. 基于改进YOLO v3模型的奶牛发情行为识别研究[J]. 农业机械学报, 2021, 52(7): 141-150. doi: 10.6041/j.issn.1000-1298.2021.07.014
    [29]

    LODKAEW T, PASUPA K, LOO C K. CowXNet: An automated cow estrus detection system[J]. Expert Systems with Applications, 2023, 211: 118550. doi: 10.1016/j.eswa.2022.118550

    [30]

    ARIKAN İ, AYAV T, SEÇKIN A Ç, et al. Estrus detection and dairy cow identification with cascade deep learning for augmented reality-ready livestock farming[J]. Sensors, 2023, 23(24): 9795. doi: 10.3390/s23249795

    [31]

    ANITAŞ Ö, GÖNCÜ S. Investigation of body secretions as bioindicators in cattle estrus detection[J]. Turkish Journal of Veterinary & Animal Sciences, 2020, 44(5): 1070-1086.

    [32]

    ALI A S, JACINTO J G, MΫNCHEMYER W, et al. Estrus detection in a dairy herd using an electronic nose by direct sampling on the perineal region[J]. Veterinary Sciences, 2022, 9(12): 688. doi: 10.3390/vetsci9120688

    [33]

    SUTHAR V, BURFEIND O, PATEL J, et al. Body temperature around induced estrus in dairy cows[J]. Journal of Dairy Science, 2011, 94(5): 2368-2373. doi: 10.3168/jds.2010-3858

    [34]

    ROELOFS J, VAN ERP-VAN DER KOOIJ E. Estrus detection tools and their applicability in cattle: Recent and perspectival situation[J]. Animal Reproduction, 2018, 12(3): 498-504.

    [35]

    AUNGIER S, ROCHE J, SHEEHY M, et al. Effects of management and health on the use of activity monitoring for estrus detection in dairy cows[J]. Journal of Dairy Science, 2012, 95(5): 2452-2466. doi: 10.3168/jds.2011-4653

    [36]

    FRIGGENS N, BJERRING M, RIDDER C, et al. Improved detection of reproductive status in dairy cows using milk progesterone measurements[J]. Reproduction in Domestic Animals, 2008, 43(S2): 113-121. doi: 10.1111/j.1439-0531.2008.01150.x

    [37] 郑国生, 施正香, 滕光辉. 中国奶牛养殖设施装备技术研究进展[J]. 中国畜牧杂志, 2019, 55(7): 169-174.
    [38]

    HAMMON H, LIERMANN W, FRIETEN D, et al. Review: Importance of colostrum supply and milk feeding intensity on gastrointestinal and systemic development in calves[J]. Animal, 2020, 14: s133-s143. doi: 10.1017/S1751731119003148

    [39]

    MEDRANO-GALARZA C, LEBLANC S J, DEVRIES T J, et al. A survey of dairy calf management practices among farms using manual and automated milk feeding systems in Canada[J]. Journal of Dairy Science, 2017, 100(8): 6872-6884. doi: 10.3168/jds.2016-12273

    [40]

    KENNY D, FITZSIMONS C, WATERS S, et al. Invited review: Improving feed efficiency of beef cattle: The current state of the art and future challenges[J]. Animal, 2018, 12(9): 1815-1826. doi: 10.1017/S1751731118000976

    [41]

    RAYNOR E J, DERNER J D, SODER K J, et al. Noseband sensor validation and behavioural indicators for assessing beef cattle grazing on extensive pastures[J]. Applied Animal Behaviour Science, 2021, 242: 105402. doi: 10.1016/j.applanim.2021.105402

    [42]

    PEZESHKI A, STORDEUR P, WALLEMACQ H, et al. Variation of inflammatory dynamics and mediators in primiparous cows after intramammary challenge with Escherichia coli[J]. Veterinary Research, 2011, 42. doi: 10.1186/1297-9716-42-15.

    [43]

    VAN HERTEM T, BAHR C, TELLO A S, et al. Lameness detection in dairy cattle: Single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing[J]. Animal, 2016, 10(9): 1525-1532. doi: 10.1017/S1751731115001457

    [44]

    JIANG B, SONG H, HE D. Lameness detection of dairy cows based on a double normal background statistical model[J]. Computers and Electronics in Agriculture, 2019, 158: 140-149. doi: 10.1016/j.compag.2019.01.025

    [45]

    VIAZZI S, BAHR C, VAN HERTEM T, et al. Comparison of a three-dimensional and two-dimensional camera system for automated measurement of back posture in dairy cows[J]. Computers and Electronics in Agriculture, 2014, 100: 139-147. doi: 10.1016/j.compag.2013.11.005

    [46]

    RODRÍGUEZ A, OLIVARES F, DESCOUVIERES P, et al. Thermographic assessment of hoof temperature in dairy cows with different mobility scores[J]. Livestock Science, 2016, 184: 92-96. doi: 10.1016/j.livsci.2015.12.006

    [47]

    STOKES J, LEACH K, MAIN D, et al. An investigation into the use of infrared thermography (IRT) as a rapid diagnostic tool for foot lesions in dairy cattle[J]. Veterinary Journal, 2012, 193(3): 674-678. doi: 10.1016/j.tvjl.2012.06.052

    [48]

    ALSAAOD M, BÜSCHER W. Detection of hoof lesions using digital infrared thermography in dairy cows[J]. Journal of Dairy Science, 2012, 95(2): 735-742. doi: 10.3168/jds.2011-4762

    [49]

    NEETHIRAJAN S, TUTEJA S K, HUANG S T, et al. Recent advancement in biosensors technology for animal and livestock health management[J]. Biosensors and Bioelectronics, 2017, 98: 398-407. doi: 10.1016/j.bios.2017.07.015

    [50]

    GLOGENER P, KRAUSE M, KATZER J, et al. Prolonged corrosion stability of a microchip sensor implant during in vivo exposure[J]. Biosensors, 2018, 8(1): 13. doi: 10.3390/bios8010013

    [51]

    GRIESCHE C, BAEUMNER A J. Biosensors to support sustainable agriculture and food safety[J]. TrAC Trends in Analytical Chemistry, 2020, 128: 115906. doi: 10.1016/j.trac.2020.115906

    [52]

    VIDIC J, MANZANO M, CHANG C M, et al. Advanced biosensors for detection of pathogens related to livestock and poultry[J]. Veterinary Research, 2017, 48: 11. doi: 10.1186/s13567-017-0418-5

    [53]

    ZENG Z, ZENG F, HAN X, et al. Real-time monitoring of environmental parameters in a commercial gestating sow house using a zigbee-based wireless sensor network[J]. Applied Sciences, 2021, 11(3): 972. doi: 10.3390/app11030972

    [54]

    LELIVELD L M C, BRANDOLESE C, GROTTO M, et al. Real-time automatic integrated monitoring of barn environment and dairy cattle behaviour: Technical implementation and evaluation on three commercial farms[J]. Computers and Electronics in Agriculture, 2024, 216: 108499. doi: 10.1016/j.compag.2023.108499

图(4)  /  表(2)
计量
  • 文章访问数:  774
  • HTML全文浏览量:  172
  • PDF下载量:  107
  • 被引次数: 0
出版历程
  • 收稿日期:  2024-05-21
  • 网络出版日期:  2024-07-03
  • 发布日期:  2024-07-07
  • 刊出日期:  2024-08-07

目录

    /

    返回文章
    返回