vetpressАграрная наукаAgrarian science0869-81552686-701XРедакция журнала "Аграрная наука"10.32634/0869-8155-2024-381-4-114-122vetpress-3035Research ArticleАГРОИНЖЕНЕРИЯ И ПИЩЕВЫЕ ТЕХНОЛОГИИAGROENGINEERING AND FOOD TECHNOLOGIESОбзор исследований и технологий, применимых для цифровизации процесса оценки экстерьера животных в мясном и молочном животноводствеReview of researches and technologies applicable to digitalization of the process of assessing the exterior of meat and dairy animalshttps://orcid.org/0000-0002-2511-7526ЮрочкаС. С.YurochkaS. S.

Сергей Сергеевич Юрочка, кандидат технических наук, старший научный сотрудник

1-й Институтский проезд, 5, Москва, 109428

Sergey Sergeevich Yurochka, Candidate of Engineering Sciences, Senior Researcher 

5 1st Institute Passage, Moscow, 109428

   

yssvim@yandex.ruhttps://orcid.org/0000-0002-4332-9274ХакимовА. Р.KhakimovA. R.

Артём Рустамович Хакимов младший научный сотрудник

1-й Институтский проезд, 5, Москва, 109428

Artem Rustamovich Khakimov Junior Researcher 

5 1st Institute Passage, Moscow, 109428

   

arty.hv@gmail.comhttps://orcid.org/0000-0001-8769-8365ПавкинД. Ю.PavkinD.  Yu.

Дмитрий Юрьевич Павкин, кандидат технических наук, старший научный сотрудник

1-й Институтский проезд, 5, Москва, 109428

Dmitry Yurievich Pavkin, Candidate of Engineering Sciences, Senior Researcher 

5 1st Institute Passage, Moscow, 109428

   

dimqaqa@mail.ruhttps://orcid.org/0000-0002-3028-5081БазаевС. О.BazaevS. O.

Савр Олегович Базаев, кандидат сельскохозяйственных наук, научный сотрудник

1-й Институтский проезд, 5, Москва, 109428

Savr Olegovich Bazaev, Candidate of Agricultural Sciences, Researcher 

5 1st Institute Passage, Moscow, 109428

   

sbazaeff@yandex.ruhttps://orcid.org/0000-0003-2407-4584КомковИ. В.KomkovI. V.

Илья Владимирович Комков, магистрант, специалист

1-й Институтский проезд, 5, Москва, 109428

Ilya Vladimirovich Komkov, Graduate Student, Specialist 

5 1st Institute Passage, Moscow, 109428

   

ilyakomkov10@yandex.ruФедеральный научный агроинженерный центр ВИМРоссияFederal Scientific Agroengineering Center VIMRussian Federation20242504202404114122Copyright © Юрочка С.С., Хакимов А.Р., Павкин Д.Ю., Базаев С.О., Комков И.В., 20242024Юрочка С.С., Хакимов А.Р., Павкин Д.Ю., Базаев С.О., Комков И.В.Yurochka S.S., Khakimov A.R., Pavkin D.Y., Bazaev S.O., Komkov I.V.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.vetpress.ru/jour/article/view/3035

Для повышения эффективности животноводства ученые разрабатывают информационные и коммуникационные технологии, направленные на цифровизацию процесса оценки экстерьера животных. Этот обзор должен улучшить понимание этапов разработки систем, применимых в цифровизации процесса оценки экстерьера животных и использующих машинное зрение и нейросети глубокого обучения. Поиск был сосредоточен на нескольких вопросах: системы машинного зрения; обучающие наборы данных; системы сбора изображений; модели глубокого обучения; нейросети для обучения; параметры производительности и оценки систем. Машинное зрение является инновационным решением благодаря сочетанию датчиков и нейросетей, реализуя бесконтактный способ оценки условий содержания скота, поскольку камеры могут заменить наблюдения человеком. Используются два подхода к получению трехмерных изображений для задач цифровизации в животноводстве: съемка животных с помощью одной 3D-камеры, закрепленной в одном месте, и съемка с разных точек с использованием нескольких 3D-камер, которые фиксируют изображения животных и отдельные части их тел, таких как вымя. Особенности, извлекаемые из изображений, называемые дорсальными чертами, используются в качестве входных данных для моделей. В изученных публикациях использовались различные модели глубокого обучения, включая CNN, DNN, R-CNN и SSD в зависимости от задачи. Аналогично и нейросети, такие как EfficientNet, ShapeNet, DeepLabCut и RefineDet, были использованы для мониторинга здоровья животных, тогда как GoogleNet, AlexNet, NasNet, CapsNet, LeNet и ERFNet в основном используются для целей идентификации.

To increase the efficiency of livestock farming, scientists are developing information and communication technologies aimed at digitalizing the process of assessing the exterior of animals. This review should improve understanding of the development steps of systems applicable to the digitalization of animal conformation assessment using computer vision and deep learning neural networks. The search focused on several topics: computer vision systems; training datasets; image acquisition systems; deep learning models; neural networks for training; performance parameters and system evaluation. Machine vision is an innovative solution by combining sensors and neural networks, providing a non-contact way to assess livestock conditions as cameras can replace human observation. Two approaches are used to obtain three-dimensional images for digitalization tasks in animal husbandry: shooting animals using one 3D camera fixed in one place, and shooting from different points using several 3D cameras that record images of animals and individual parts of their bodies, such like an udder. The features extracted from the images, called dorsal features, are used as input to the models. The reviewed publications used a variety of deep learning models, including CNN, DNN, R-CNN, and SSD, depending on the task. Similarly, neural networks such as EfficientNet, ShapeNet, DeepLabCut and RefineDet have been mainly used for animal health monitoring, while GoogleNet, AlexNet, NasNet, CapsNet, LeNet and ERFNet are mainly used for identification purposes.

молочное и мясное животноводствоцифровизацияселекциябонитировочные работынейросетитрехмерные изображенияdairy and meat farmingdigitalizationselectionvaluation workneural networksthree-dimensional imagesИсследование выполнено за счет гранта Российского научного фонда № 23-76-10041. https://rscf.ru/project/23-76-10041/The research was carried out with funds from the Russian Science Foundation grant No. 23-76-10041. https://rscf.ru/project/23-76-10041/ReferencesЛобачевский Я.П., Дорохов А.С. Цифровые технологии и роботизированные технические средства для сельского хозяйства. Сельскохозяйственные машины и технологии. 2021; 15(4): 6–10. https://doi.org/10.22314/2073-7599-2021-15-4-6-10Lobachevskiy Ya.P., Dorokhov A.S. Digital technologies and robotic devices in the agriculture. Agricultural Machinery and Technologies. 2021; 15(4): 6–10 (in Russian). https://doi.org/10.22314/2073-7599-2021-15-4-6-10Ценч Ю.С. Научно-технический потенциал как главный фактор развития механизации сельского хозяйства. Сельскохозяйственные машины и технологии. 2022; 16(2): 4–13. https://doi.org/10.22314/2073-7599-2022-16-2-4-13Tsench Yu.S. Scientific and Technological Potential as the Main Factor for Agricultural Mechanization Development. Agricultural Machinery and Technologies. 2022; 16(2): 4–13 (in Russian). https://doi.org/10.22314/2073-7599-2022-16-2-4-13Павкин Д.Ю., Юрочка С.С., Хакимов А.Р., Довлатов И.М. Разработка модульной системы цифровизации бонитировочных работ. Сельскохозяйственные машины и технологии. 2022; 16(4): 54–59. https://doi.org/10.22314/2073-7599-2022-16-4-54-59Pavkin D.Yu., Yurochka S.S., Khakimov A.R., Dovlatov I.M. Development of a Modular System for Digitalization of Appraisal Process. Agricultural Machinery and Technologies. 2022; 16(4): 54–59 (in Russian). https://doi.org/10.22314/2073-7599-2022-16-4-54-59Meen G.H., Schellekens M.A., Slegers M.H.M., Leenders N.L.G., van Erp-van der Kooij E., Noldus L.P.J.J. Sound analysis in dairy cattle vocalisation as a potential welfare monitor. Computers and Electronics in Agriculture. 2015; 118: 111–115. https://doi.org/10.1016/j.compag.2015.08.028Meen G.H., Schellekens M.A., Slegers M.H.M., Leenders N.L.G., van Erp-van der Kooij E., Noldus L.P.J.J. Sound analysis in dairy cattle vocalisation as a potential welfare monitor. Computers and Electronics in Agriculture. 2015; 118: 111–115. https://doi.org/10.1016/j.compag.2015.08.028Halachmi I., Guarino M. Editorial: Precision livestock farming: a “per animal” approach using advanced monitoring technologies. animal. 2016; 10(9): 1482–1483. https://doi.org/10.1017/S1751731116001142Halachmi I., Guarino M. Editorial: Precision livestock farming: a “per animal” approach using advanced monitoring technologies. animal. 2016; 10(9): 1482–1483. https://doi.org/10.1017/S1751731116001142Banhazi T.M. et al. Precision Livestock Farming: An international review of scientific and commercial aspects. International Journal of Agricultural and Biological Engineering. 2012; 5(3): 1. https://doi.org/10.3965/j.ijabe.20120503.001Banhazi T.M. et al. Precision Livestock Farming: An international review of scientific and commercial aspects. International Journal of Agricultural and Biological Engineering. 2012; 5(3): 1. https://doi.org/10.3965/j.ijabe.20120503.001Wilkins J.F., McKiernan W.A., Walmsley B.J., McPhee M.J. Automated data capture using laser technology to enhance live cattle assessment and description. Australian Farm Business Management Journal. 2015; 12: 70–77. https://doi.org/10.22004/ag.econ.284945Wilkins J.F., McKiernan W.A., Walmsley B.J., McPhee M.J. Automated data capture using laser technology to enhance live cattle assessment and description. Australian Farm Business Management Journal. 2015; 12: 70–77. https://doi.org/10.22004/ag.econ.284945Hansen M.F., Smith M.L., Smith L.N., Jabbar K.A., Forbes D. Automated monitoring of dairy cow body condition, mobility and weight using a single 3D video capture device. Computers in Industry. 2018; 98: 14–22. https://doi.org/10.1016/j.compind.2018.02.011Hansen M.F., Smith M.L., Smith L.N., Jabbar K.A., Forbes D. Automated monitoring of dairy cow body condition, mobility and weight using a single 3D video capture device. Computers in Industry. 2018; 98: 14–22. https://doi.org/10.1016/j.compind.2018.02.011OʼLeary N., Leso L., Buckley F., Kenneally J., McSweeney D., Shalloo L. Validation of an Automated Body Condition Scoring System Using 3D Imaging. Agriculture. 2020; 10(6): 246. https://doi.org/10.3390/agriculture10060246OʼLeary N., Leso L., Buckley F., Kenneally J., McSweeney D., Shalloo L. Validation of an Automated Body Condition Scoring System Using 3D Imaging. Agriculture. 2020; 10(6): 246. https://doi.org/10.3390/agriculture10060246Gaudioso V., Sanz-Ablanedo E., Lomillos J.M., Alonso M.E., Javares-Morillo L., Rodríguez P. “Photozoometer”: A new photogrammetric system for obtaining morphometric measurements of elusive animals. Livestock Science. 2014; 165: 147–156. https://doi.org/10.1016/j.livsci.2014.03.028Gaudioso V., Sanz-Ablanedo E., Lomillos J.M., Alonso M.E., Javares-Morillo L., Rodríguez P. “Photozoometer”: A new photogrammetric system for obtaining morphometric measurements of elusive animals. Livestock Science. 2014; 165: 147–156. https://doi.org/10.1016/j.livsci.2014.03.028Ruchay A., Kober V., Dorofeev K., Kolpakov V., Miroshnikov S. Accurate body measurement of live cattle using three depth cameras and non-rigid 3D shape recovery. Computers and Electronics in Agriculture. 2020; 179: 105821. https://doi.org/10.1016/j.compag.2020.105821Ruchay A., Kober V., Dorofeev K., Kolpakov V., Miroshnikov S. Accurate body measurement of live cattle using three depth cameras and non-rigid 3D shape recovery. Computers and Electronics in Agriculture. 2020; 179: 105821. https://doi.org/10.1016/j.compag.2020.105821Martins B.M. et al. Estimating body weight, body condition score, and type traits in dairy cows using three dimensional cameras and manual body measurements. Livestock Science. 2020; 236: 104054. https://doi.org/10.1016/j.livsci.2020.104054Martins B.M. et al. Estimating body weight, body condition score, and type traits in dairy cows using three dimensional cameras and manual body measurements. Livestock Science. 2020; 236: 104054. https://doi.org/10.1016/j.livsci.2020.104054Kamchen S.G., dos Santos E.F., Lopes L.B., Vendrusculo L.G., Condotta I.C.F.S. Application of depth sensor to estimate body mass and morphometric assessment in Nellore heifers. Livestock Science. 2021; 245: 104442. https://doi.org/10.1016/j.livsci.2021.104442Kamchen S.G., dos Santos E.F., Lopes L.B., Vendrusculo L.G., Condotta I.C.F.S. Application of depth sensor to estimate body mass and morphometric assessment in Nellore heifers. Livestock Science. 2021; 245: 104442. https://doi.org/10.1016/j.livsci.2021.104442Zin T.T., Seint P.T., Tin P., Horii Y., Kobayashi I. Body Condition Score Estimation Based on Regression Analysis Using a 3D Camera. Sensors. 2020; 20(13): 3705. https://doi.org/10.3390/s20133705Zin T.T., Seint P.T., Tin P., Horii Y., Kobayashi I. Body Condition Score Estimation Based on Regression Analysis Using a 3D Camera. Sensors. 2020; 20(13): 3705. https://doi.org/10.3390/s20133705Van Hertem T. et al. Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behavior and performance sensing. animal. 2016; 10(9): 1525–1532. https://doi.org/10.1017/s1751731115001457Van Hertem T. et al. Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behavior and performance sensing. animal. 2016; 10(9): 1525–1532. https://doi.org/10.1017/s1751731115001457Schlageter-Tello A., Van Hertem T., Bokkers E.A.M., Viazzi S., Bahr C., Lokhorst K. Performance of human observers and an automatic 3-dimensional computer-vision-based locomotion scoring method to detect lameness and hoof lesions in dairy cows. Journal of Dairy Science. 2018; 101(7): 6322–6335. https://doi.org/10.3168/jds.2017-13768Schlageter-Tello A., Van Hertem T., Bokkers E.A.M., Viazzi S., Bahr C., Lokhorst K. Performance of human observers and an automatic 3-dimensional computer-vision-based locomotion scoring method to detect lameness and hoof lesions in dairy cows. Journal of Dairy Science. 2018; 101(7): 6322–6335. https://doi.org/10.3168/jds.2017-13768Banhazi T.M., Black J.L. Precision Livestock Farming: A Suite of Electronic Systems to Ensure the Application of Best Practice Management on Livestock Farms. Australian Journal of Multi-Disciplinary Engineering. 2009; 7(1): 1–14. https://doi.org/10.1080/14488388.2009.11464794Banhazi T.M., Black J.L. Precision Livestock Farming: A Suite of Electronic Systems to Ensure the Application of Best Practice Management on Livestock Farms. Australian Journal of Multi-Disciplinary Engineering. 2009; 7(1): 1–14. https://doi.org/10.1080/14488388.2009.11464794Tan C., Sun F., Kong T., Zhang W., Yang C., Liu C. A Survey on Deep Transfer Learning. The 27th International Conference on Artificial Neural Networks (ICANN 2018). 2018. https://doi.org/10.48550/arXiv.1808.01974Tan C., Sun F., Kong T., Zhang W., Yang C., Liu C. A Survey on Deep Transfer Learning. The 27th International Conference on Artificial Neural Networks (ICANN 2018). 2018. https://doi.org/10.48550/arXiv.1808.01974Psota E.T. et al. Development and validation of a neural network for the automated detection of horn flies on cattle. Computers and Electronics in Agriculture. 2021; 180: 105927. https://doi.org/10.1016/j.compag.2020.105927Psota E.T. et al. Development and validation of a neural network for the automated detection of horn flies on cattle. Computers and Electronics in Agriculture. 2021; 180: 105927. https://doi.org/10.1016/j.compag.2020.105927Jiang B., Wu Q., Yin X., Wu D., Song H., He D. FLYOLOv3 deep learning for key parts of dairy cow body detection. Computers and Electronics in Agriculture. 2019; 166: 104982. https://doi.org/10.1016/j.compag.2019.104982Jiang B., Wu Q., Yin X., Wu D., Song H., He D. FLYOLOv3 deep learning for key parts of dairy cow body detection. Computers and Electronics in Agriculture. 2019; 166: 104982. https://doi.org/10.1016/j.compag.2019.104982Weber F.d.L. et al. Recognition of Pantaneira cattle breed using computer vision and convolutional neural networks. Computers and Electronics in Agriculture. 2020; 175: 105548. https://doi.org/10.1016/j.compag.2020.105548Weber F.d.L. et al. Recognition of Pantaneira cattle breed using computer vision and convolutional neural networks. Computers and Electronics in Agriculture. 2020; 175: 105548. https://doi.org/10.1016/j.compag.2020.105548Kang X., Zhang X.D., Liu G. Accurate detection of lameness in dairy cattle with computer vision: A new and individualized detection strategy based on the analysis of the supporting phase. Journal of Dairy Science. 2020; 103(11): 10628–10638. https://doi.org/10.3168/jds.2020-18288Kang X., Zhang X.D., Liu G. Accurate detection of lameness in dairy cattle with computer vision: A new and individualized detection strategy based on the analysis of the supporting phase. Journal of Dairy Science. 2020; 103(11): 10628–10638. https://doi.org/10.3168/jds.2020-18288Xudong Z., Xi K., Ningning F., Gang L. Automatic recognition of dairy cow mastitis from thermal images by a deep learning detector. Computers and Electronics in Agriculture. 2020; 178: 105754. https://doi.org/10.1016/j.compag.2020.105754Xudong Z., Xi K., Ningning F., Gang L. Automatic recognition of dairy cow mastitis from thermal images by a deep learning detector. Computers and Electronics in Agriculture. 2020; 178: 105754. https://doi.org/10.1016/j.compag.2020.105754Gjergji M. et al. Deep Learning Techniques for Beef Cattle Body Weight Prediction. 2020 International Joint Conference on Neural Networks (IJCNN). 2020; 1–8. https://doi.org/10.1109/IJCNN48605.2020.9207624Gjergji M. et al. Deep Learning Techniques for Beef Cattle Body Weight Prediction. 2020 International Joint Conference on Neural Networks (IJCNN). 2020; 1–8. https://doi.org/10.1109/IJCNN48605.2020.9207624Jabbar K.A., Hansen M.F., Smith M.L., Smith L.N. Early and non-intrusive lameness detection in dairy cows using 3-dimensional video. Biosystems Engineering. 2017; 153: 63–69. https://doi.org/10.1016/j.biosystemseng.2016.09.017Jabbar K.A., Hansen M.F., Smith M.L., Smith L.N. Early and non-intrusive lameness detection in dairy cows using 3-dimensional video. Biosystems Engineering. 2017; 153: 63–69. https://doi.org/10.1016/j.biosystemseng.2016.09.017Guo H., Ma X., Ma Q., Wang K., Su W., Zhu D. LSSA_CAU: An interactive 3d point clouds analysis software for body measurement of livestock with similar forms of cows or pigs. Computers and Electronics in Agriculture. 2017; 138: 60–68. https://doi.org/10.1016/j.compag.2017.04.014Guo H., Ma X., Ma Q., Wang K., Su W., Zhu D. LSSA_CAU: An interactive 3d point clouds analysis software for body measurement of livestock with similar forms of cows or pigs. Computers and Electronics in Agriculture. 2017; 138: 60–68. https://doi.org/10.1016/j.compag.2017.04.014Slob N., Catal C., Kassahun A., Application of machine learning to improve dairy farm management: A systematic literature review. Preventive Veterinary Medicine. 2021; 187: 105237. https://doi.org/10.1016/j.prevetmed.2020.105237Slob N., Catal C., Kassahun A., Application of machine learning to improve dairy farm management: A systematic literature review. Preventive Veterinary Medicine. 2021; 187: 105237. https://doi.org/10.1016/j.prevetmed.2020.105237Lovarelli D., Bacenetti J., Guarino M. A review on dairy cattle farming: Is precision livestock farming the compromise for an environmental, economic and social sustainable production? Journal of Cleaner Production. 2020; 262: 121409. https://doi.org/10.1016/j.jclepro.2020.121409Lovarelli D., Bacenetti J., Guarino M. A review on dairy cattle farming: Is precision livestock farming the compromise for an environmental, economic and social sustainable production? Journal of Cleaner Production. 2020; 262: 121409. https://doi.org/10.1016/j.jclepro.2020.121409García R., Aguilar J., Toro M., Pinto A., Rodríguez P. A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture. 2020; 179: 105826. https://doi.org/10.1016/j.compag.2020.105826García R., Aguilar J., Toro M., Pinto A., Rodríguez P. A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture. 2020; 179: 105826. https://doi.org/10.1016/j.compag.2020.105826Harrison S.M. et al. Three-dimensional growth of bovine hoof as recorded by carbon stable isotope ratios. Rapid Communications in Mass Spectrometry. 2007; 21(24): 3971–3976. https://doi.org/10.1002/rcm.3309Harrison S.M. et al. Three-dimensional growth of bovine hoof as recorded by carbon stable isotope ratios. Rapid Communications in Mass Spectrometry. 2007; 21(24): 3971–3976. https://doi.org/10.1002/rcm.3309Berckmans D. General introduction to precision livestock farming. Animal Frontiers. 2017; 7(1): 6–11. https://doi.org/10.2527/af.2017.0102Berckmans D. General introduction to precision livestock farming. Animal Frontiers. 2017; 7(1): 6–11. https://doi.org/10.2527/af.2017.0102Neethirajan S. Recent advances in wearable sensors for animal health management. Sensing and Bio-Sensing Research. 2017; 12: 15–29. https://doi.org/10.1016/j.sbsr.2016.11.004Neethirajan S. Recent advances in wearable sensors for animal health management. Sensing and Bio-Sensing Research. 2017; 12: 15–29. https://doi.org/10.1016/j.sbsr.2016.11.004Viazzi C. et al. Comparison of a three-dimensional and two-dimensional camera system for automated measurement of back posture in dairy cows. Computers and Electronics in Agriculture. 2014; 100: 139–147. https://doi.org/10.1016/j.compag.2013.11.005Viazzi C. et al. Comparison of a three-dimensional and two-dimensional camera system for automated measurement of back posture in dairy cows. Computers and Electronics in Agriculture. 2014; 100: 139–147. https://doi.org/10.1016/j.compag.2013.11.005Song X., Bokkers E.A.M., van Mourik S., Groot Koerkamp P.W.G., van der Tol P.P.J. Automated body condition scoring of dairy cows using 3-dimensional feature extraction from multiple body regions. Journal of Dairy Science. 2019; 102(5): 4294–4308. https://doi.org/10.3168/jds.2018-15238Song X., Bokkers E.A.M., van Mourik S., Groot Koerkamp P.W.G., van der Tol P.P.J. Automated body condition scoring of dairy cows using 3-dimensional feature extraction from multiple body regions. Journal of Dairy Science. 2019; 102(5): 4294–4308. https://doi.org/10.3168/jds.2018-15238Salau J., Bauer U., Haas J.H., Thaller G., Harms J., Junge W. Quantification of the effects of fur, fur color, and velocity on Time-Of-Flight technology in dairy production. SpringerPlus. 2015; 4: 144. https://doi.org/10.1186/s40064-015-0903-0Salau J., Bauer U., Haas J.H., Thaller G., Harms J., Junge W. Quantification of the effects of fur, fur color, and velocity on Time-Of-Flight technology in dairy production. SpringerPlus. 2015; 4: 144. https://doi.org/10.1186/s40064-015-0903-0Cominotte A. et al. Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases. Livestock Science. 2020; 232: 103904. https://doi.org/10.1016/j.livsci.2019.103904Cominotte A. et al. Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases. Livestock Science. 2020; 232: 103904. https://doi.org/10.1016/j.livsci.2019.103904Nasirahmadi A., Edwards S.A., Sturm B. Implementation of machine vision for detecting behaviour of cattle and pigs. Livestock Science. 2017; 202: 25–38. https://doi.org/10.1016/j.livsci.2017.05.014Nasirahmadi A., Edwards S.A., Sturm B. Implementation of machine vision for detecting behaviour of cattle and pigs. Livestock Science. 2017; 202: 25–38. https://doi.org/10.1016/j.livsci.2017.05.014Wang Z., Shadpour S., Chan E., Rotondo V., Wood K.M., Tulpan D. ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images. Journal of Animal Science. 2021; 99(2): skab022. https://doi.org/10.1093/jas/skab022Wang Z., Shadpour S., Chan E., Rotondo V., Wood K.M., Tulpan D. ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images. Journal of Animal Science. 2021; 99(2): skab022. https://doi.org/10.1093/jas/skab022Salau J., Haas J.H., Junge W., Thaller G. Extrinsic calibration of a multi-Kinect camera scanning passage for measuring functional traits in dairy cows. Biosystems Engineering. 2016; 151: 409–424. https://doi.org/10.1016/j.biosystemseng.2016.10.008Salau J., Haas J.H., Junge W., Thaller G. Extrinsic calibration of a multi-Kinect camera scanning passage for measuring functional traits in dairy cows. Biosystems Engineering. 2016; 151: 409–424. https://doi.org/10.1016/j.biosystemseng.2016.10.008Salau J., Haas J.H., Junge W., Thaller G. Determination of Body Parts in Holstein Friesian Cows Comparing Neural Networks and k Nearest Neighbour Classification. Animals. 2020; 11(1): 50. https://doi.org/10.3390/ani11010050Salau J., Haas J.H., Junge W., Thaller G. Determination of Body Parts in Holstein Friesian Cows Comparing Neural Networks and k Nearest Neighbour Classification. Animals. 2020; 11(1): 50. https://doi.org/10.3390/ani11010050Salau J., Haas J.H., Junge W., Thaller G. Automated calculation of udder depth and rear leg angle in Holstein-Friesian cows using a multi-Kinect cow scanning system. Biosystems Engineering. 2017; 160: 154–169. https://doi.org/10.1016/j.biosystemseng.2017.06.006Salau J., Haas J.H., Junge W., Thaller G. Automated calculation of udder depth and rear leg angle in Holstein-Friesian cows using a multi-Kinect cow scanning system. Biosystems Engineering. 2017; 160: 154–169. https://doi.org/10.1016/j.biosystemseng.2017.06.006Salau J., Haas J.H., Thaller G., Leisen M., Junge W. Developing a multi-Kinect-system for monitoring in dairy cows: object recognition and surface analysis using wavelets. Animal. 2016; 10(9): 1513–1524. https://doi.org/10.1017/s1751731116000021Salau J., Haas J.H., Thaller G., Leisen M., Junge W. Developing a multi-Kinect-system for monitoring in dairy cows: object recognition and surface analysis using wavelets. Animal. 2016; 10(9): 1513–1524. https://doi.org/10.1017/s1751731116000021Van Hertem T. et al. Automatic lameness detection based on consecutive 3D-video recordings. Biosystems Engineering. 2014; 119: 108–116. https://doi.org/10.1016/j.biosystemseng.2014.01.009Van Hertem T. et al. Automatic lameness detection based on consecutive 3D-video recordings. Biosystems Engineering. 2014; 119: 108–116. https://doi.org/10.1016/j.biosystemseng.2014.01.009Le Cozler Y. et al. Volume and surface area of Holstein dairy cows calculated from complete 3D shapes acquired using a high-precision scanning system: Interest for body weight estimation. Computers and Electronics in Agriculture. 2019; 165: 104977. https://doi.org/10.1016/j.compag.2019.104977Le Cozler Y. et al. Volume and surface area of Holstein dairy cows calculated from complete 3D shapes acquired using a high-precision scanning system: Interest for body weight estimation. Computers and Electronics in Agriculture. 2019; 165: 104977. https://doi.org/10.1016/j.compag.2019.104977Pezzuolo A., Guarino M., Sartori L., Marinello F. A Feasibility Study on the Use of a Structured Light Depth-Camera for Three-Dimensional Body Measurements of Dairy Cows in Free-Stall Barns. Sensors. 2018; 18(2): 673. https://doi.org/10.3390/s18020673Pezzuolo A., Guarino M., Sartori L., Marinello F. A Feasibility Study on the Use of a Structured Light Depth-Camera for Three-Dimensional Body Measurements of Dairy Cows in Free-Stall Barns. Sensors. 2018; 18(2): 673. https://doi.org/10.3390/s18020673Shigeta M., Ike R., Takemura H., Ohwada H. Automatic Measurement and Determination of Body Condition Score of Cows Based on 3D Images Using CNN. Journal of Robotics and Mechatronics. 2018; 30(2): 206–213. https://doi.org/10.20965/jrm.2018.p0206Shigeta M., Ike R., Takemura H., Ohwada H. Automatic Measurement and Determination of Body Condition Score of Cows Based on 3D Images Using CNN. Journal of Robotics and Mechatronics. 2018; 30(2): 206–213. https://doi.org/10.20965/jrm.2018.p0206Spoliansky R., Edan Y., Parmet Y., Halachmi I. Development of automatic body condition scoring using a low-cost 3-dimensional Kinect camera. Journal of Dairy Science. 2016; 99(9): 7714–7725. https://doi.org/10.3168/jds.2015-10607Spoliansky R., Edan Y., Parmet Y., Halachmi I. Development of automatic body condition scoring using a low-cost 3-dimensional Kinect camera. Journal of Dairy Science. 2016; 99(9): 7714–7725. https://doi.org/10.3168/jds.2015-10607Fischer A., Luginbühl T., Delattre L., Delouard J.M., Faverdin P. Rear shape in 3 dimensions summarized by principal component analysis is a good predictor of body condition score in Holstein dairy cows. Journal of Dairy Science. 2015; 98(7): 4465–4476. https://doi.org/10.3168/jds.2014-8969Fischer A., Luginbühl T., Delattre L., Delouard J.M., Faverdin P. Rear shape in 3 dimensions summarized by principal component analysis is a good predictor of body condition score in Holstein dairy cows. Journal of Dairy Science. 2015; 98(7): 4465–4476. https://doi.org/10.3168/jds.2014-8969Okura F., Ikuma S., Makihara Y., Muramatsu D., Nakada K., Yagi Y. RGB-D video-based individual identification of dairy cows using gait and texture analyses. Computers and Electronics in Agriculture. 2019; 165: 104944. https://doi.org/10.1016/j.compag.2019.104944Okura F., Ikuma S., Makihara Y., Muramatsu D., Nakada K., Yagi Y. RGB-D video-based individual identification of dairy cows using gait and texture analyses. Computers and Electronics in Agriculture. 2019; 165: 104944. https://doi.org/10.1016/j.compag.2019.104944Le Cozler Y. et al. High-precision scanning system for complete 3D cow body shape imaging and analysis of morphological traits. Computers and Electronics in Agriculture. 2019; 157: 447–453. https://doi.org/10.1016/j.compag.2019.01.019Le Cozler Y. et al. High-precision scanning system for complete 3D cow body shape imaging and analysis of morphological traits. Computers and Electronics in Agriculture. 2019; 157: 447–453. https://doi.org/10.1016/j.compag.2019.01.019Huang L.W., Li S., Zhu A., Fan X., Zhang C., Wang H. Non-Contact Body Measurement for Qinchuan Cattle with LiDAR Sensor. Sensors. 2018; 18(9): 3014. https://doi.org/10.3390/s18093014Huang L.W., Li S., Zhu A., Fan X., Zhang C., Wang H. Non-Contact Body Measurement for Qinchuan Cattle with LiDAR Sensor. Sensors. 2018; 18(9): 3014. https://doi.org/10.3390/s18093014Huang L. et al. Body Dimension Measurements of Qinchuan Cattle with Transfer Learning from LiDAR Sensing. Sensors. 2019; 19(22): 5046. https://doi.org/10.3390/s19225046Huang L. et al. Body Dimension Measurements of Qinchuan Cattle with Transfer Learning from LiDAR Sensing. Sensors. 2019; 19(22): 5046. https://doi.org/10.3390/s19225046Guesgen M.J., Bench C.J. Using kinematics to detect micro-behavioural changes relative to ovulation in naturally cycling tie-stall dairy heifers. Livestock Science. 2018; 207: 126–132. https://doi.org/10.1016/j.livsci.2017.11.019Guesgen M.J., Bench C.J. Using kinematics to detect micro-behavioural changes relative to ovulation in naturally cycling tie-stall dairy heifers. Livestock Science. 2018; 207: 126–132. https://doi.org/10.1016/j.livsci.2017.11.019Van Hertem T. et al. Implementation of an automatic 3D vision monitor for dairy cow locomotion in a commercial farm. Biosystems Engineering. 2018; 173: 166–175. https://doi.org/10.1016/j.biosystemseng.2017.08.011Van Hertem T. et al. Implementation of an automatic 3D vision monitor for dairy cow locomotion in a commercial farm. Biosystems Engineering. 2018; 173: 166–175. https://doi.org/10.1016/j.biosystemseng.2017.08.011Nir O., Parmet Y., Werner D., Adin G., Halachmi I. 3D Computer-vision system for automatically estimating heifer height and body mass. Biosystems Engineering. 2018; 173: 4–10. https://doi.org/10.1016/j.biosystemseng.2017.11.014Nir O., Parmet Y., Werner D., Adin G., Halachmi I. 3D Computer-vision system for automatically estimating heifer height and body mass. Biosystems Engineering. 2018; 173: 4–10. https://doi.org/10.1016/j.biosystemseng.2017.11.014Lomillos J.M., Alonso M.E. Morphometric Characterization of the Lidia Cattle Breed. Animals. 2020; 10(7): 1180. https://doi.org/10.3390/ani10071180Lomillos J.M., Alonso M.E. Morphometric Characterization of the Lidia Cattle Breed. Animals. 2020; 10(7): 1180. https://doi.org/10.3390/ani10071180Song X., Bokkers E.A.M., van der Tol P.P.J., Groot Koerkamp P.W.G., van Mourik S. Automated body weight prediction of dairy cows using 3-dimensional vision. Journal of Dairy Science. 2018; 101(5): 4448–4459. https://doi.org/10.3168/jds.2017-13094Song X., Bokkers E.A.M., van der Tol P.P.J., Groot Koerkamp P.W.G., van Mourik S. Automated body weight prediction of dairy cows using 3-dimensional vision. Journal of Dairy Science. 2018; 101(5): 4448–4459. https://doi.org/10.3168/jds.2017-13094Brownlee J. Deep Learning for Computer Vision: Image Classification, Object Detection, and Face Recognition in Python. 2019; xviii, 544.Brownlee J. Deep Learning for Computer Vision: Image Classification, Object Detection, and Face Recognition in Python. 2019; xviii, 544.Wu D., Yin X., Jiang B., Jiang M., Li Z., Song H. Detection of the respiratory rate of standing cows by combining the Deeplab v3 + semantic segmentation model with the phase-based video magnification algorithm. Biosystems Engineering. 2020; 192: 72–89. https://doi.org/10.1016/j.biosystemseng.2020.01.012Wu D., Yin X., Jiang B., Jiang M., Li Z., Song H. Detection of the respiratory rate of standing cows by combining the Deeplab v3 + semantic segmentation model with the phase-based video magnification algorithm. Biosystems Engineering. 2020; 192: 72–89. https://doi.org/10.1016/j.biosystemseng.2020.01.012Qiao Y., Su D., Kong H., Sukkarieh S., Lomax S., Clark C. BiLSTM-based Individual Cattle Identification for Automated Precision Livestock Farming. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). 2020; 967–972. https://doi.org/10.1109/CASE48305.2020.9217026Qiao Y., Su D., Kong H., Sukkarieh S., Lomax S., Clark C. BiLSTM-based Individual Cattle Identification for Automated Precision Livestock Farming. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). 2020; 967–972. https://doi.org/10.1109/CASE48305.2020.9217026

The authors declare that there are no conflicts of interest present.