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Siachos N, Lennox M, Anagnostopoulos A, Griffiths BE, Neary JM, Smith RF, Oikonomou G. Development and validation of a fully automated 2-dimensional imaging system generating body condition scores for dairy cows using machine learning. J Dairy Sci 2024; 107:2499-2511. [PMID: 37977440 DOI: 10.3168/jds.2023-23894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 10/30/2023] [Indexed: 11/19/2023]
Abstract
Monitoring body condition score (BCS) is a useful management tool to estimate the energy reserves of an individual cow or a group of cows. The aim of this study was to develop and evaluate the performance of a fully automated 2-dimensional imaging system using a machine learning algorithm to generate real-time BCS for dairy cows. Two separate datasets were used for training and testing. The training dataset included 34,150 manual BCS (MAN_BCS) assigned by 5 experienced veterinarians during 35 visits at 7 dairy farms. Ordinal regression methods and deep learning architecture were used when developing the algorithm. Subsequently, the testing dataset was used to evaluate the developed BCS prediction algorithm on 4 of the participating farms. An experienced human assessor (HA1) visited these farms and performed 8 whole-milking-herd BCS sessions. Each farm was visited twice, allowing for 30 d (±2 d) to pass between visits. The MAN_BCS assigned by HA1 were considered the ground truth data. At the end of the validation study, MAN_BCS were merged with the stored automated BCS (AI_BCS), resulting in a testing dataset of 9,657 single BCS. A total of 3,817 cows in the testing dataset were scored twice 30 d (±2 d) apart, and the change in their BCS (ΔBCS) was calculated. A subset of cows at one farm were scored twice on consecutive days to evaluate the within-observer agreement of both the human assessor and the system. The manual BCS of 2 more assessors (HA2 and HA3) were used to assess the interobserver agreement between humans. Finally, we also collected ultrasound measurements of backfat thickness (BFT) from 111 randomly selected cows with available MAN_BCS and AI_BCS. Using the testing dataset, intra- and interobserver agreement for single BCS and ΔBCS were estimated by calculating the simple percentage agreement (PA) at 3 error levels and the weighted kappa (κw) for the exact agreement. A Bland-Altman plot was constructed to visualize the systematic and proportional bias. The association between MAN_BCS and AI_BCS and the BFT was assessed with Passing-Bablok regressions. The system had an almost perfect repeatability with a κw of 0.99. The agreement between MAN_BCS and AI_BCS was substantial, with an overall κw of 0.69. The overall PA at the exact, ± 0.25-unit, and ± 0.50-unit BCS error range between MAN_BCS and AI_BCS was 44.4%, 84.6%, and 94.8%, respectively, and greater than the PA obtained between HA1 and HA3. The Bland-Altman plot revealed a minimal systematic bias of -0.09 with a proportional bias at the extreme scores. Furthermore, despite the low κw of 0.20, the overall PA at the exact and ± 0.25-unit of BCS error range between MAN_BCS and AI_BCS regarding the ΔBCS was 45.7 and 88.2%, respectively. A strong linear relationship was observed between BFT and AI_BCS (ρ = 0.75), although weaker than that between BFT and MAN_BCS (ρ = 0.91). The system was able to predict single BCS and ΔBCS with satisfactory accuracy, comparable to that obtained between trained human scorers.
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Affiliation(s)
- N Siachos
- Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Leahurst Campus, CH64 7TE, United Kingdom
| | - M Lennox
- CattleEye Ltd., The Innovation Centre, Queens Road, Belfast BT3 9DT, United Kingdom
| | - A Anagnostopoulos
- Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Leahurst Campus, CH64 7TE, United Kingdom
| | - B E Griffiths
- Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Leahurst Campus, CH64 7TE, United Kingdom
| | - J M Neary
- Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Leahurst Campus, CH64 7TE, United Kingdom
| | - R F Smith
- Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Leahurst Campus, CH64 7TE, United Kingdom
| | - G Oikonomou
- Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Leahurst Campus, CH64 7TE, United Kingdom.
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2
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Peng Y, Peng Z, Zou H, Liu M, Hu R, Xiao J, Liao H, Yang Y, Huo L, Wang Z. A dynamic individual yak heifer live body weight estimation method using the YOLOv8 network and body parameter detection algorithm. J Dairy Sci 2024:S0022-0302(24)00496-X. [PMID: 38395405 DOI: 10.3168/jds.2023-24065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 01/20/2024] [Indexed: 02/25/2024]
Abstract
Live body weight (LBW) is one of the most important parameters for supervising the growth and development of livestock. The yak (Bos grunniens) is a special species of cattle that lives on the Qinghai-Tibetan Plateau. Yaks are more untamed than regular cattle breeds, thus it is more challenging to measure their LBW. In this study, a YOLOv8 yak detection and LBW estimation models were used to automatically estimate yak LBW in real-time. First, the proper posture (normal posture) and individual yak identification was confirmed and then the YOLOv8 detection model was used for LBW estimation from 2-dimensional (2D) images. Yak LBW was estimated through yak body parameter extraction and a simple linear regression between the estimated yak LBW and the actual measured yak LBW. The results showed that the overall detection performance of yak normal yak posture was described by precision, recall, and mean Average Precision 50 (mAP50) indicators, reaching 81.8, 86.0, and 90.6%, respectively. The best yak identification results were represented by precision, recall, and mAP50 values of 97.8, 96.4, and 99.0%, respectively. The yak LBW estimation model achieved better results for the 12 mo old yaks with shorter hair with R2, root mean square error (RMSE), mean absolute percentage error (MAPE), and Multiple R values of 0.96, 2.43 kg, 1.69%, and 0.98, respectively. The results demonstrate that yak LBW can be estimated and monitored in real-time using this approach. This study has the potential to be used for daily yak LBW monitoring in an unstressed manner and to save considerable labor resources for large-scale livestock farms. In the future, to reduce the limitations caused by the impacts of yak hair and light condition data sets of dairy cows and yaks of different ages will be used to improve and generalize the model.
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Affiliation(s)
- Yingqi Peng
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, China.
| | - Zhaoyuan Peng
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, China
| | - Huawei Zou
- Animal Nutrition Institute, Sichuan Agricultural University, China
| | - Meiqi Liu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, China
| | - Rui Hu
- Animal Nutrition Institute, Sichuan Agricultural University, China
| | - Jianxin Xiao
- Animal Nutrition Institute, Sichuan Agricultural University, China
| | - Haocheng Liao
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, China
| | - Yuxiang Yang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, China
| | - Lushun Huo
- Animal Nutrition Institute, Sichuan Agricultural University, China
| | - Zhisheng Wang
- Animal Nutrition Institute, Sichuan Agricultural University, China.
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3
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Summerfield GI, De Freitas A, van Marle-Koster E, Myburgh HC. Automated Cow Body Condition Scoring Using Multiple 3D Cameras and Convolutional Neural Networks. SENSORS (BASEL, SWITZERLAND) 2023; 23:9051. [PMID: 38005439 PMCID: PMC10675635 DOI: 10.3390/s23229051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 10/28/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023]
Abstract
Body condition scoring is an objective scoring method used to evaluate the health of a cow by determining the amount of subcutaneous fat in a cow. Automated body condition scoring is becoming vital to large commercial dairy farms as it helps farmers score their cows more often and more consistently compared to manual scoring. A common approach to automated body condition scoring is to utilise a CNN-based model trained with data from a depth camera. The approaches presented in this paper make use of three depth cameras placed at different positions near the rear of a cow to train three independent CNNs. Ensemble modelling is used to combine the estimations of the three individual CNN models. The paper aims to test the performance impact of using ensemble modelling with the data from three separate depth cameras. The paper also looks at which of these three cameras and combinations thereof provide a good balance between computational cost and performance. The results of this study show that utilising the data from three depth cameras to train three separate models merged through ensemble modelling yields significantly improved automated body condition scoring accuracy compared to a single-depth camera and CNN model approach. This paper also explored the real-world performance of these models on embedded platforms by comparing the computational cost to the performance of the various models.
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Affiliation(s)
- Gary I. Summerfield
- Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0028, South Africa; (A.D.F.); (H.C.M.)
| | - Allan De Freitas
- Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0028, South Africa; (A.D.F.); (H.C.M.)
| | | | - Herman C. Myburgh
- Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0028, South Africa; (A.D.F.); (H.C.M.)
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4
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Voogt AM, Schrijver RS, Temürhan M, Bongers JH, Sijm DTHM. Opportunities for Regulatory Authorities to Assess Animal-Based Measures at the Slaughterhouse Using Sensor Technology and Artificial Intelligence: A Review. Animals (Basel) 2023; 13:3028. [PMID: 37835634 PMCID: PMC10571985 DOI: 10.3390/ani13193028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/19/2023] [Accepted: 09/20/2023] [Indexed: 10/15/2023] Open
Abstract
Animal-based measures (ABMs) are the preferred way to assess animal welfare. However, manual scoring of ABMs is very time-consuming during the meat inspection. Automatic scoring by using sensor technology and artificial intelligence (AI) may bring a solution. Based on review papers an overview was made of ABMs recorded at the slaughterhouse for poultry, pigs and cattle and applications of sensor technology to measure the identified ABMs. Also, relevant legislation and work instructions of the Dutch Regulatory Authority (RA) were scanned on applied ABMs. Applications of sensor technology in a research setting, on farm or at the slaughterhouse were reported for 10 of the 37 ABMs identified for poultry, 4 of 32 for cattle and 13 of 41 for pigs. Several applications are related to aspects of meat inspection. However, by European law meat inspection must be performed by an official veterinarian, although there are exceptions for the post mortem inspection of poultry. The examples in this study show that there are opportunities for using sensor technology by the RA to support the inspection and to give more insight into animal welfare risks. The lack of external validation for multiple commercially available systems is a point of attention.
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Affiliation(s)
- Annika M. Voogt
- Office for Risk Assessment & Research (BuRO), Netherlands Food and Consumer Product Safety Authority (NVWA), P.O. Box 43006, 3540 AA Utrecht, The Netherlands
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Neethirajan S. Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation. SENSORS (BASEL, SWITZERLAND) 2023; 23:7045. [PMID: 37631580 PMCID: PMC10458494 DOI: 10.3390/s23167045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/04/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023]
Abstract
This technical note critically evaluates the transformative potential of Artificial Intelligence (AI) and sensor technologies in the swiftly evolving dairy livestock export industry. We focus on the novel application of the Internet of Things (IoT) in long-distance livestock transportation, particularly in livestock enumeration and identification for precise traceability. Technological advancements in identifying behavioral patterns in 'shy feeder' cows and real-time weight monitoring enhance the accuracy of long-haul livestock transportation. These innovations offer benefits such as improved animal welfare standards, reduced supply chain inaccuracies, and increased operational productivity, expanding market access and enhancing global competitiveness. However, these technologies present challenges, including individual animal customization, economic analysis, data security, privacy, technological adaptability, training, stakeholder engagement, and sustainability concerns. These challenges intertwine with broader ethical considerations around animal treatment, data misuse, and the environmental impacts. By providing a strategic framework for successful technology integration, we emphasize the importance of continuous adaptation and learning. This note underscores the potential of AI, IoT, and sensor technologies to shape the future of the dairy livestock export industry, contributing to a more sustainable and efficient global dairy sector.
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Affiliation(s)
- Suresh Neethirajan
- Department of Animal Science and Aquaculture, Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada
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6
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Nieckarz Z, Nowicki J, Labocha K, Pawlak K. A novel method for automatically analysing the activity of fast-moving animals: a case study of Callimico goeldii monkeys housed in a zoological garden. Sci Rep 2023; 13:11476. [PMID: 37455271 PMCID: PMC10350455 DOI: 10.1038/s41598-023-38472-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 07/09/2023] [Indexed: 07/18/2023] Open
Abstract
Behavioural indices are recognised as important criteria for assessing animal welfare. One of the basic animal behaviours included in ethograms is their activity. The assessment of fast-moving animals, performed by humans using the visual observation method, is difficult and not very objective. Therefore, the aim of the research was to develop a method of automated analysis of animal activity, particularly useful in the observation of quick and lively individuals, and to prove its suitability for assessing the behaviour of fast-moving animals. A method of automatically assessing animal activity was developed using digital image analysis, with the Python programming language and the OpenCV library being the foundational tools. The research model was Callimico goeldii monkeys housed in a zoological garden. This method has been proved to correlate well (Rs = 0.76) with the visual method of animal behaviour analysis. The developed automatic evaluation of animal behaviour is many times faster than visual analysis, and it enables precise assessment of the daily activity of fast-moving groups of animals. The use of this system makes it possible to obtain an activity index with sub-second resolution, which allows it to be used in online mode as a detector of abnormal animal activity, e.g. early detection of illnesses or sudden events that are manifested by increased or decreased activity in relation to the standard activity pattern.
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Affiliation(s)
- Zenon Nieckarz
- Department of Experimental Computer Physics, Institute of Physics, Jagiellonian University in Cracow, Cracow, Poland
| | - Jacek Nowicki
- Department of Genetics, Animal Breeding and Ethology, University of Agriculture in Cracow, Cracow, Poland
| | | | - Krzysztof Pawlak
- Department of Zoology and Animal Welfare, University of Agriculture in Cracow, Aleja Adama Mickiewicza 24/28, 30-059, Kraków, Poland.
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7
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Ositanwosu OE, Huang Q, Liang Y, Nwokoye CH. Automatic measurement and prediction of Chinese Grown Pigs weight using multilayer perceptron neural networks. Sci Rep 2023; 13:2573. [PMID: 36782002 PMCID: PMC9925736 DOI: 10.1038/s41598-023-28433-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 01/18/2023] [Indexed: 02/15/2023] Open
Abstract
The knowledge of body size/weight is necessary for the general growth enhancement of swine as well as for making informed decisions that concern their health, productivity, and yield. Therefore, this work aims to automate the collection of pigs' body parameters using images from Kinect V2 cameras, and the development of Multilayer Perceptron Neural Network (MLP NN) models to predict their weight. The dataset obtained using 3D light depth cameras contains 9980 pigs across the S21 and S23 breeds, and then grouped into 70:15:15 training, testing, and validation sets, respectively. Initially, two MLP models were built and evaluations revealed that model 1 outperformed model 2 in predicting pig weights, with root mean squared error (RMSE) values of 5.5 and 6.0 respectively. Moreover, employing a normalized dataset, two new models (3 and 4) were developed and trained. Subsequently, models 2, 3, and 4 performed significantly better with a RMSE value of 5.29 compared to model 1, which has a RMSE value of 6.95. Model 3 produced an intriguing discovery i.e. accurate forecasting of pig weights using just two characteristics, age and abdominal circumference, and other error values show corresponding results.
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Affiliation(s)
- Obiajulu Emenike Ositanwosu
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China
- Department of Computer Science, Nnamdi Azikiwe University, P.M.B. 5025, Awka, Nigeria
| | - Qiong Huang
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China.
- Guangzhou Key Laboratory of Intelligent Agriculture, South China Agricultural University, Guangzhou, 510642, China.
| | - Yun Liang
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China
- Guangzhou Key Laboratory of Intelligent Agriculture, South China Agricultural University, Guangzhou, 510642, China
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8
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Gebreyesus G, Milkevych V, Lassen J, Sahana G. Supervised learning techniques for dairy cattle body weight prediction from 3D digital images. Front Genet 2023; 13:947176. [PMID: 36685975 PMCID: PMC9849234 DOI: 10.3389/fgene.2022.947176] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 11/29/2022] [Indexed: 01/06/2023] Open
Abstract
Introduction: The use of automation and sensor-based systems in livestock production allows monitoring of individual cows in real-time and provides the possibility of early warning systems to take necessary management actions against possible anomalies. Among the different RT monitoring parameters, body weight (BW) plays an important role in tracking the productivity and health status. Methods: In this study, various supervised learning techniques representing different families of methods in the machine learning space were implemented and compared for performance in the prediction of body weight from 3D image data in dairy cows. A total of 83,011 records of contour data from 3D images and body weight measurements taken from a total of 914 Danish Holstein and Jersey cows from 3 different herds were used for the predictions. Various metrics including Pearson's correlation coefficient (r), the root mean squared error (RMSE), and the mean absolute percentage error (MAPE) were used for robust evaluation of the various supervised techniques and to facilitate comparison with other studies. Prediction was undertaken separately within each breed and subsequently in a combined multi-breed dataset. Results and discussion: Despite differences in predictive performance across the different supervised learning techniques and datasets (breeds), our results indicate reasonable prediction accuracies with mean correlation coefficient (r) as high as 0.94 and MAPE and RMSE as low as 4.0 % and 33.0 (kg), respectively. In comparison to the within-breed analyses (Jersey, Holstein), prediction using the combined multi-breed data set resulted in higher predictive performance in terms of high correlation coefficient and low MAPE. Additional tests showed that the improvement in predictive performance is mainly due to increase in data size from combining data rather than the multi-breed nature of the combined data. Of the different supervised learning techniques implemented, the tree-based group of supervised learning techniques (Catboost, AdaBoost, random forest) resulted in the highest prediction performance in all the metrics used to evaluate technique performance. Reported prediction errors in our study (RMSE and MAPE) are one of the lowest in the literature for prediction of BW using image data in dairy cattle, highlighting the promising predictive value of contour data from 3D images for BW in dairy cows under commercial farm conditions.
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Affiliation(s)
- Grum Gebreyesus
- Aarhus University, Center for Quantitative Genetics and Genomics, Aarhus, Denmark,*Correspondence: Grum Gebreyesus,
| | - Viktor Milkevych
- Aarhus University, Center for Quantitative Genetics and Genomics, Aarhus, Denmark
| | - Jan Lassen
- Aarhus University, Center for Quantitative Genetics and Genomics, Aarhus, Denmark,Viking Genetics, Randers, Denmark
| | - Goutam Sahana
- Aarhus University, Center for Quantitative Genetics and Genomics, Aarhus, Denmark
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9
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Two- and Three-Dimensional Computer Vision Techniques for More Reliable Body Condition Scoring. DAIRY 2022. [DOI: 10.3390/dairy4010001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
This article identifies the essential technologies and considerations for the development of an Automated Cow Monitoring System (ACMS) which uses 3D camera technology for the assessment of Body Condition Score (BCS). We present a comparison of a range of common techniques at the different developmental stages of Computer Vision including data pre-processing and the implementation of Deep Learning for both 2D and 3D data formats commonly captured by 3D cameras. This research focuses on attaining better reliability from one deployment of an ACMS to the next and proposes a Geometric Deep Learning (GDL) approach and evaluating model performance for robustness from one farm to another in the presence of background, farm, herd, camera pose and cow pose variabilities.
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10
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Yu M, Zheng H, Xu D, Shuai Y, Tian S, Cao T, Zhou M, Zhu Y, Zhao S, Li X. Non-contact detection method of pregnant sows backfat thickness based on two-dimensional images. Anim Genet 2022; 53:769-781. [PMID: 35989407 DOI: 10.1111/age.13248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 07/16/2022] [Accepted: 07/27/2022] [Indexed: 11/27/2022]
Abstract
Since sow backfat thickness (BFT) is highly correlated with its service life and reproductive effectiveness, dynamic monitoring of BFT is a critical component of large-scale sow farm productivity. Existing contact measures of sow BFT have their problems including, high measurement intensity and sows' stress reaction, low biological safety, and difficulty in meeting the requirements for multiple measurements. This article presents a two-dimensional (2D) image-based approach for determining the BFT of pregnant sows when combined with the backfat growth rate (BGR). The 2D image features of sows extracted by convolutional neural networks (CNN) and the artificially defined phenotypic features of sows such as hip width, hip height, body length, hip height-width ratio, length-width ratio, and waist-hip ratio, were used respectively, combined with BGR, to construct a prediction model for sow BFT using support vector regression (SVR). Following testing and comparison, it was shown that using CNN to extract features from images could effectively replace artificially defined features, BGR contributed to the model's accuracy improvement. The CNN-BGR-SVR model performed the best, with R2 of 0.72 and mean absolute error of 1.21 mm, and root mean square error of 1.50 mm, and mean absolute percentage error of 7.57%. The results demonstrated that the CNN-BGR-SVR model based on 2D images was capable of detecting sow BFT, establishing a new reference for non-contact sow BFT detection technology.
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Affiliation(s)
- Mengyuan Yu
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, Hubei, China.,Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Hongya Zheng
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, Hubei, China.,Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Dihong Xu
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, Hubei, China.,Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Yonghui Shuai
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, Hubei, China.,Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Shanfeng Tian
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, Hubei, China.,Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Tingjin Cao
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, Hubei, China.,Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Mingyan Zhou
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, Hubei, China.,Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Yuhua Zhu
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, Hubei, China.,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Shuhong Zhao
- College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Xuan Li
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, Hubei, China.,Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, Hubei, China.,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
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11
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Alwis SD, Hou Z, Zhang Y, Na MH, Ofoghi B, Sajjanhar A. A survey on smart farming data, applications and techniques. COMPUT IND 2022. [DOI: 10.1016/j.compind.2022.103624] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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12
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KOJIMA T, OISHI K, AOKI N, MATSUBARA Y, UETE T, FUKUSHIMA Y, INOUE G, SATO S, SHIRAISHI T, HIROOKA H, MASUDA T. Estimation of beef cow body condition score: a machine learning approach using three-dimensional image data and a simple approach with heart girth measurements. Livest Sci 2022. [DOI: 10.1016/j.livsci.2021.104816] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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13
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Malliaroudaki MI, Watson NJ, Ferrari R, Nchari LN, Gomes RL. Energy management for a net zero dairy supply chain under climate change. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2022.01.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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14
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KAYA M, BARDAKÇIOĞLU HE. Estimation of body weight and body condition score in dairy cows by digital image analysis method. MEHMET AKIF ERSOY ÜNIVERSITESI VETERINER FAKÜLTESI DERGISI 2021. [DOI: 10.24880/maeuvfd.963188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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15
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Precision Technologies to Address Dairy Cattle Welfare: Focus on Lameness, Mastitis and Body Condition. Animals (Basel) 2021; 11:ani11082253. [PMID: 34438712 PMCID: PMC8388461 DOI: 10.3390/ani11082253] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/28/2021] [Accepted: 07/28/2021] [Indexed: 01/28/2023] Open
Abstract
Simple Summary The welfare of farm animals is a growing concern in the EU and across the world. In milk production, there is a strong need to assess the welfare of dairy cows. One of the most sound assessment initiatives has been practiced using protocols developed by the Welfare Quality project. These protocols mainly support the assessment of cow welfare with animal-based indicators. However, evaluating these indicators is time-consuming and expensive, so using precision livestock farming (PLF) solutions is a way forward and is becoming a reality in the dairy industry. This review presents advances in PLF solutions, particularly in the last five years, and for assessing the animal-based indicators of lameness, mastitis, and body condition in dairy cattle farming. Abstract Specific animal-based indicators that can be used to predict animal welfare have been the core of protocols for assessing the welfare of farm animals, such as those produced by the Welfare Quality project. At the same time, the contribution of technological tools for the accurate and real-time assessment of farm animal welfare is also evident. The solutions based on technological tools fit into the precision livestock farming (PLF) concept, which has improved productivity, economic sustainability, and animal welfare in dairy farms. PLF has been adopted recently; nevertheless, the need for technological support on farms is getting more and more attention and has translated into significant scientific contributions in various fields of the dairy industry, but with an emphasis on the health and welfare of the cows. This review aims to present the recent advances of PLF in dairy cow welfare, particularly in the assessment of lameness, mastitis, and body condition, which are among the most relevant animal-based indications for the welfare of cows. Finally, a discussion is presented on the possibility of integrating the information obtained by PLF into a welfare assessment framework.
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Berry DP, Evans RD, Kelleher MM. Prediction of genetic merit for live weight and body condition score in dairy cows using routinely available linear type and carcass data. J Dairy Sci 2021; 104:6885-6896. [PMID: 33773797 DOI: 10.3168/jds.2021-20154] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 02/16/2021] [Indexed: 11/19/2022]
Abstract
Accurate estimates of genetic merit for both live weight and body condition score (BCS) could be useful additions to both national- and herd-breeding programs. Although recording live weight and BCS is not technologically arduous, data available for use in routine genetic evaluations are generally lacking. The objective of the present study was to explore the usefulness of routinely recorded data, namely linear type traits (which also included BCS but only assessed visually) and carcass traits in the pursuit of genetic evaluations for both live weight and BCS in dairy cows. The data consisted of on-farm records of live weight and BCS (assessed using both visual and tactile cues) from 33,242 dairy cows in 201 commercial Irish herds. These data were complemented with information on 6 body-related linear type traits (i.e., stature, angularity, chest width, body depth, BCS, and rump width) and 3 cull cow carcass measures (i.e., carcass weight, conformation, and fat cover) on a selection of these animals plus close relatives. (Co)variance components were estimated using animal linear mixed models. The genetic correlation between the type traits stature, angularity, body depth, chest width, rump width, and visually-assessed BCS with live weight was 0.68, -0.28, 0.43, 0.64, 0.61, and 0.44, respectively. The genetic correlation between angularity and BCS measured on farm (based on both visual and tactile appraisal) was -0.79; the genetic and phenotypic correlation between BCS assessed visually as part of the linear assessment with BCS assessed by producers using both tactile and visual cues was 0.90 and 0.27, respectively. The genetic (phenotypic) correlation between cull cow carcass weight and live weight was 0.81 (0.21), and the genetic (phenotypic) correlation between cull cow carcass fat cover and BCS assessed on live cows was 0.44 (0.12). Estimated breeding values (EBV) for live weight and BCS in a validation population of cows were generated using a multitrait evaluation with observations for just the type traits, just the carcass traits, and both the type traits and carcass traits; the EBV were compared with the respective live weight and BCS phenotypic observations. The regression of phenotypic live weight on its EBV from the multitrait evaluations was 1.00 (i.e., the expectation) when the EBV was generated using just linear type trait data, but less than 1 (0.83) when using just carcass data. However, the regression changed across parities and stages of lactation. The partial correlation (after adjusting for contemporary group, parity by stage of lactation, heterosis, and recombination loss) between phenotypic live weight and EBV for live weight estimated using the 3 different scenarios (i.e., type only, carcass only, type plus carcass) ranged from 0.38 to 0.43. Although the prediction of phenotypic BCS from its respective EBV was relatively good when using just the linear type trait data (regression coefficient of 0.83 with a partial correlation of 0.22), the predictive ability of BCS EBV based on just carcass data was poor and should not be used. Overall, linear type trait data are a useful source of information to predict live weight and BCS with minimal additional predictive value from also including carcass data. Nonetheless, in the absence of linear type trait data, information on carcass traits can be useful in predicting genetic merit for mature cow live weight. Prediction of cow BCS from cow carcass data is not recommended.
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Affiliation(s)
- D P Berry
- Teagasc, Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy P61 P302, Co. Cork, Ireland.
| | - R D Evans
- Irish Cattle Breeding Federation, Highfield House, Shinagh, Bandon P72 X050, Co. Cork, Ireland
| | - M M Kelleher
- Irish Cattle Breeding Federation, Highfield House, Shinagh, Bandon P72 X050, Co. Cork, Ireland
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Weight and volume estimation of poultry and products based on computer vision systems: a review. Poult Sci 2021; 100:101072. [PMID: 33752071 PMCID: PMC8010860 DOI: 10.1016/j.psj.2021.101072] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 01/14/2021] [Accepted: 02/04/2021] [Indexed: 01/10/2023] Open
Abstract
The appearance, size, and weight of poultry meat and eggs are essential for production economics and vital in the poultry sector. These external characteristics influence their market price and consumers' preference and choice. With technological developments, there is an increase in the application and importance of vision systems in the agricultural sector. Computer vision has become a promising tool in the real-time automation of poultry weighing and processing systems. Owing to its noninvasive and nonintrusive nature and its capacity to present a wide range of information, computer vision systems can be applied in the size, mass, volume determination, and sorting and grading of poultry products. This review article gives a detailed summary of the current advances in measuring poultry products' external characteristics based on computer vision systems. An overview of computer vision systems is discussed and summarized. A comprehensive presentation of the application of computer vision-based systems for assessing poultry meat and eggs was provided, that is, weight and volume estimation, sorting, and classification. Finally, the challenges and potential future trends in size, weight, and volume estimation of poultry products are reported.
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Salzer Y, Honig HH, Shaked R, Abeles E, Kleinjan-Elazary A, Berger K, Jacoby S, Fishbain B, Kendler S. Towards on-site automatic detection of noxious events in dairy cows. Appl Anim Behav Sci 2021. [DOI: 10.1016/j.applanim.2021.105260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Berry DP, Kelleher MM. Short communication: Differences in genetic merit for visually-assessed body condition score materialises as phenotypic differences in tactile-based body condition score in commercial dairy cows. Animal 2021; 15:100181. [PMID: 33610518 DOI: 10.1016/j.animal.2021.100181] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 01/08/2021] [Accepted: 01/11/2021] [Indexed: 11/26/2022] Open
Abstract
Body condition score (BCS) is a known risk factor for cow health and well-being. Many different BCS scales and systems for assessment exist;while the scales used for assessing BCS vary, differences in how BCS is assessed (i.e., visual versus visual plus tactile) and the extent of training and experience of the assessor (i.e., professional linear classifiers versus producers) also contributes to the underlying variability. Registered dairy cows globally are routinely assessed for linear type traits which describe biological extremes in the morphological attributes; BCS and a correlated trait angularity are within this suite of traits assessed. These linear-type data are used to generate estimates of genetic merit (predicted transmitting ability), but how these estimates manifest themselves as phenotypic differences when assessed by producers on commercial multiparous cows has never been quantified. To evaluate this, 58440 phenotypic BCS records from 48823 lactations in 38608 cows were used. Associations were undertaken using linear mixed models relating phenotypic BCS to genetic merit after accounting for nuisance factors. Differences in genetic merit for either BCS or angularity (assessed visually by professionals on a 1 to 9 scale just once during lactation in primiparous registered cows) translated to phenotypic difference in BCS (assessed by producers using both tactile and visual assessment on a 1 to 5 scale across lactation in commercial dairy cows). The partial correlation between test phenotypic BCS and genetic merit for either BCS or angularity was 0.13 and 0.10, respectively. Based on the model coefficients estimated in the present study, the mean expected difference in phenotypic BCS on a 1 to 5 scale between the top and bottom 10% on genetic merit for BCS or angularity was 0.28 and 0.31 units, respectively. Results from the present study clearly provide confidence that genetic merit for BCS or angularity based on a single visual assessment in primiparous cows is useful to breed for cows of better body condition, irrespective of stage of lactation or parity.
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Affiliation(s)
- D P Berry
- Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy P61 P302, Co. Cork, Ireland.
| | - M M Kelleher
- Irish Cattle Breeding Federation, Highfield House, Shinagh, Bandon P72 X050, Co. Cork, Ireland
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Kang X, Zhang XD, Liu G. A Review: Development of Computer Vision-Based Lameness Detection for Dairy Cows and Discussion of the Practical Applications. SENSORS 2021; 21:s21030753. [PMID: 33499381 PMCID: PMC7866151 DOI: 10.3390/s21030753] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 01/18/2021] [Accepted: 01/20/2021] [Indexed: 01/29/2023]
Abstract
The computer vision technique has been rapidly adopted in cow lameness detection research due to its noncontact characteristic and moderate price. This paper attempted to summarize the research progress of computer vision in the detection of lameness. Computer vision lameness detection systems are not popular on farms, and the accuracy and applicability still need to be improved. This paper discusses the problems and development prospects of this technique from three aspects: detection methods, verification methods and application implementation. The paper aims to provide the reader with a summary of the literature and the latest advances in the field of computer vision detection of lameness in dairy cows.
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Affiliation(s)
- Xi Kang
- Key Lab of Modern Precision Agriculture System Integration Research, Ministry of Education of China, China Agricultural University, Beijing 100083, China; (X.K.); (X.D.Z.)
- Key Lab of Agricultural Information Acquisition Technology, Ministry of Agricultural of China, China Agricultural University, Beijing 100083, China
| | - Xu Dong Zhang
- Key Lab of Modern Precision Agriculture System Integration Research, Ministry of Education of China, China Agricultural University, Beijing 100083, China; (X.K.); (X.D.Z.)
- Key Lab of Agricultural Information Acquisition Technology, Ministry of Agricultural of China, China Agricultural University, Beijing 100083, China
| | - Gang Liu
- Key Lab of Modern Precision Agriculture System Integration Research, Ministry of Education of China, China Agricultural University, Beijing 100083, China; (X.K.); (X.D.Z.)
- Key Lab of Agricultural Information Acquisition Technology, Ministry of Agricultural of China, China Agricultural University, Beijing 100083, China
- Correspondence: ; Tel.: +86-010-62736741
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Kim S, Hidaka Y. Breathing Pattern Analysis in Cattle Using Infrared Thermography and Computer Vision. Animals (Basel) 2021; 11:ani11010207. [PMID: 33466995 PMCID: PMC7830257 DOI: 10.3390/ani11010207] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 01/14/2021] [Accepted: 01/14/2021] [Indexed: 11/16/2022] Open
Abstract
Breathing patterns can be considered a vital sign providing health information. Infrared thermography is used to evaluate breathing patterns because it is non-invasive. Our study used not only sequence temperature data but also RGB images to gain breathing patterns in cattle. Mask R-CNN was used to detect the ROI (region of interest, nose) in the cattle RGB images. Mask segmentation from the ROI detection was applied to the corresponding temperature data. Finally, to visualize the breathing pattern, we calculated the temperature values in the ROI by averaging all temperature values in the ROI. The results in this study show 76% accuracy with Mask R-CNN in detecting cattle noses. With respect to the temperature calculation methods, the averaging method showed the most appropriate breathing pattern compared to other methods (maximum temperature in the ROI and integrating all temperature values in the ROI). Finally, we compared the breathing pattern from the averaging method and that from the thermal image observation and found them to be highly correlated (R2 = 0.91). This method is not labor-intensive, can handle big data, and is accurate. In addition, we expect that the characteristics of the method might enable the analysis of temperature data from various angles.
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Affiliation(s)
- Sueun Kim
- Correspondence: ; Tel.: +81-985-58-7791
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Zhang X, Liu G, Jing L, Chen S. Automated Measurement of Heart Girth for Pigs Using Two Kinect Depth Sensors. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3848. [PMID: 32664221 PMCID: PMC7411683 DOI: 10.3390/s20143848] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 07/01/2020] [Accepted: 07/08/2020] [Indexed: 11/16/2022]
Abstract
The heart girth parameter is an important indicator reflecting the growth and development of pigs that provides critical guidance for the optimization of healthy pig breeding. To overcome the heavy workloads and poor adaptability of traditional measurement methods currently used in pig breeding, this paper proposes an automated pig heart girth measurement method using two Kinect depth sensors. First, a two-view pig depth image acquisition platform is established for data collection; the two-view point clouds after preprocessing are registered and fused by feature-based improved 4-Point Congruent Set (4PCS) method. Second, the fused point cloud is pose-normalized, and the axillary contour is used to automatically extract the heart girth measurement point. Finally, this point is taken as the starting point to intercept the circumferential perpendicular to the ground from the pig point cloud, and the complete heart girth point cloud is obtained by mirror symmetry. The heart girth is measured along this point cloud using the shortest path method. Using the proposed method, experiments were conducted on two-view data from 26 live pigs. The results showed that the heart girth measurement absolute errors were all less than 4.19 cm, and the average relative error was 2.14%, which indicating a high accuracy and efficiency of this method.
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Affiliation(s)
- Xinyue Zhang
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China;
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, and Rural Affairs, China Agricultural University, Beijing 100083, China
| | - Gang Liu
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China;
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, and Rural Affairs, China Agricultural University, Beijing 100083, China
| | - Ling Jing
- College of Science, China Agricultural University, Beijing 100083, China;
| | - Siyao Chen
- Graduate School of Agriculture, Kyoto University, Kyoto 606-8502, Japan;
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Zin TT, Seint PT, Tin P, Horii Y, Kobayashi I. Body Condition Score Estimation Based on Regression Analysis Using a 3D Camera. SENSORS 2020; 20:s20133705. [PMID: 32630751 PMCID: PMC7374283 DOI: 10.3390/s20133705] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 06/22/2020] [Accepted: 06/29/2020] [Indexed: 01/06/2023]
Abstract
The Body Condition Score (BCS) for cows indicates their energy reserves, the scoring for which ranges from very thin to overweight. These measurements are especially useful during calving, as well as early lactation. Achieving a correct BCS helps avoid calving difficulties, losses and other health problems. Although BCS can be rated by experts, it is time-consuming and often inconsistent when performed by different experts. Therefore, the aim of our system is to develop a computerized system to reduce inconsistencies and to provide a time-saving solution. In our proposed system, the automatic body condition scoring system is introduced by using a 3D camera, image processing techniques and regression models. The experimental data were collected on a rotary parlor milking station on a large-scale dairy farm in Japan. The system includes an application platform for automatic image selection as a primary step, which was developed for smart monitoring of individual cows on large-scale farms. Moreover, two analytical models are proposed in two regions of interest (ROI) by extracting 3D surface roughness parameters. By applying the extracted parameters in mathematical equations, the BCS is automatically evaluated based on measurements of model accuracy, with one of the two models achieving a mean absolute percentage error (MAPE) of 3.9%, and a mean absolute error (MAE) of 0.13.
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Affiliation(s)
- Thi Thi Zin
- Graduate School of Engineering, University of Miyazaki, 1 Chome-1 Gakuenkibanadainishi, Miyazaki 889-2192, Japan; (P.T.S.); (P.T.)
- Correspondence:
| | - Pann Thinzar Seint
- Graduate School of Engineering, University of Miyazaki, 1 Chome-1 Gakuenkibanadainishi, Miyazaki 889-2192, Japan; (P.T.S.); (P.T.)
| | - Pyke Tin
- Graduate School of Engineering, University of Miyazaki, 1 Chome-1 Gakuenkibanadainishi, Miyazaki 889-2192, Japan; (P.T.S.); (P.T.)
| | - Yoichiro Horii
- Center for Animal Disease Control, University of Miyazaki, 1 Chome-1 Gakuenkibanadainishi, Miyazaki 889-2192, Japan;
| | - Ikuo Kobayashi
- Field Science Center, Faculty of Agriculture, University of Miyazaki, 1 Chome-1 Gakuenkibanadainishi, Miyazaki 889-2192, Japan;
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Bezdíček J, Nesvadbová A, Makarevich A, Kubovičová E. Relationship between the animal body condition and reproduction: the biotechnological aspects. Arch Anim Breed 2020; 63:203-209. [PMID: 32760787 PMCID: PMC7397717 DOI: 10.5194/aab-63-203-2020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 05/12/2020] [Indexed: 11/17/2022] Open
Abstract
The aim of this review was to evaluate the relationship between the body
condition of cows and reproduction. Reproduction was evaluated from the
viewpoint of animal husbandry traits, ovarian activity and embryo transfer.
Main emphasis was given to the review of articles from the area of
biotechnical methods (in vitro embryo production, embryo transfer). Most authors agree on the opinion that the worsening of the reproduction traits of cows is a result of changes in the body condition score (BCS) either under or over their average value. Worsening of reproduction traits was presented not only from a zootechnical viewpoint (e.g., calving interval, 56 d nonreturn rate, etc.) but also in term of ovarian activity, oocyte recovery and in vitro embryo
production. In general, the body condition of cows is an important factor
affecting female reproduction ability at the ovarian level.
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Affiliation(s)
- Jiří Bezdíček
- Faculty of Science, Palacký University Olomouc, Olomouc, 771 47, Czech Republic
| | - Andrea Nesvadbová
- Faculty of Science, Palacký University Olomouc, Olomouc, 771 47, Czech Republic
| | - Alexander Makarevich
- National Agricultural and Food Centre (NPPC), Research Institute for Animal Production Nitra, 95141 Lužianky, Slovak Republic
| | - Elena Kubovičová
- National Agricultural and Food Centre (NPPC), Research Institute for Animal Production Nitra, 95141 Lužianky, Slovak Republic
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Abstract
BACKGROUND The shape of pig scapula is complex and is important for sow robustness and health. To better understand the relationship between 3D shape of the scapula and functional traits, it is necessary to build a model that explains most of the morphological variation between animals. This requires point correspondence, i.e. a map that explains which points represent the same piece of tissue among individuals. The objective of this study was to further develop an automated computational pipeline for the segmentation of computed tomography (CT) scans to incorporate 3D modelling of the scapula, and to develop a genetic prediction model for 3D morphology. RESULTS The surface voxels of the scapula were identified on 2143 CT-scanned pigs, and point correspondence was established by predicting the coordinates of 1234 semi-landmarks on each animal, using the coherent point drift algorithm. A subsequent principal component analysis showed that the first 10 principal components covered more than 80% of the total variation in 3D shape of the scapula. Using principal component scores as phenotypes in a genetic model, estimates of heritability ranged from 0.4 to 0.8 (with standard errors from 0.07 to 0.08). To validate the entire computational pipeline, a statistical model was trained to predict scapula shape based on marker genotype data. The mean prediction reliability averaged over the whole scapula was equal to 0.18 (standard deviation = 0.05) with a higher reliability in convex than in concave regions. CONCLUSIONS Estimates of heritability of the principal components were high and indicated that the computational pipeline that processes CT data to principal component phenotypes was associated with little error. Furthermore, we showed that it is possible to predict the 3D shape of scapula based on marker genotype data. Taken together, these results show that the proposed computational pipeline closes the gap between a point cloud representing the shape of an animal and its underlying genetic components.
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Affiliation(s)
- Øyvind Nordbø
- Norsvin SA, Storhamargata 44, 2317, Hamar, Norway.
- Geno SA, Storhamargata 44, 2317, Hamar, Norway.
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Visser C, Van Marle-Köster E, Myburgh HC, De Freitas A. Phenomics for sustainable production in the South African dairy and beef cattle industry. Anim Front 2020; 10:12-18. [PMID: 32257598 PMCID: PMC7111604 DOI: 10.1093/af/vfaa003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Carina Visser
- Department of Animal and Wildlife Sciences, University of Pretoria, Hatfield, South Africa
| | - Este Van Marle-Köster
- Department of Animal and Wildlife Sciences, University of Pretoria, Hatfield, South Africa
| | - Herman C Myburgh
- Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Hatfield, South Africa
| | - Allan De Freitas
- Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Hatfield, South Africa
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Auclair-Ronzaud J, Benoist S, Dubois C, Frejaville M, Jousset T, Jaffrézic F, Wimel L, Chavatte-Palmer P. No-Contact Microchip Monitoring of Body Temperature in Yearling Horses. J Equine Vet Sci 2020; 86:102892. [DOI: 10.1016/j.jevs.2019.102892] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Revised: 12/06/2019] [Accepted: 12/07/2019] [Indexed: 12/20/2022]
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Wurtz K, Camerlink I, D’Eath RB, Fernández AP, Norton T, Steibel J, Siegford J. Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review. PLoS One 2019; 14:e0226669. [PMID: 31869364 PMCID: PMC6927615 DOI: 10.1371/journal.pone.0226669] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2019] [Accepted: 12/03/2019] [Indexed: 01/02/2023] Open
Abstract
Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced.
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Affiliation(s)
- Kaitlin Wurtz
- Department of Animal Science, Michigan State University, East Lansing, Michigan, United States of America
- * E-mail:
| | - Irene Camerlink
- Department of Farm Animals and Veterinary Public Health, Institute of Animal Welfare Science, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Richard B. D’Eath
- Animal Behaviour & Welfare, Animal and Veterinary Sciences, Scotland’s Rural College (SRUC), Edinburgh, United Kingdom
| | | | - Tomas Norton
- M3-BIORES– Measure, Model & Manage Bioresponses, KU Leuven, Leuven, Belgium
| | - Juan Steibel
- Department of Animal Science, Michigan State University, East Lansing, Michigan, United States of America
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan, United States of America
| | - Janice Siegford
- Department of Animal Science, Michigan State University, East Lansing, Michigan, United States of America
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Risk Factors and Detection of Lameness Using Infrared Thermography in Dairy Cows – A Review. ANNALS OF ANIMAL SCIENCE 2019. [DOI: 10.2478/aoas-2019-0008] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Abstract
Lameness in dairy cows is a worldwide problem, usually a consequence of hoof diseases. Hoof problems have a negative impact on animal health and welfare as well as the economy of the farm. Prevention and early diagnosis of lameness should prevent the development of the disease and consequent high costs of animal treatment. In this review, the most common causes of both infectious and noninfectious lesions are described. Susceptibility to lesions is primarily influenced by the quality of the horn. The quality of the horn is influenced by internal and external conditions such as hygiene, nutrition, hormonal changes during calving and lactation, the animal’s age or genetic predisposition. The next part of this review summarizes the basic principles and possibilities of using infrared thermography in the early detection of lameness in dairy cows.
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Huang X, Hu Z, Wang X, Yang X, Zhang J, Shi D. An Improved Single Shot Multibox Detector Method Applied in Body Condition Score for Dairy Cows. Animals (Basel) 2019; 9:ani9070470. [PMID: 31340515 PMCID: PMC6680808 DOI: 10.3390/ani9070470] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 07/15/2019] [Accepted: 07/19/2019] [Indexed: 02/03/2023] Open
Abstract
Simple Summary Body condition score (BCS) is an important work for feeding management and cow welfare on the farm. The aim of our study is to assess the BCS automatically and replace the traditional manual method. In this study, we firstly built a non-contact and no-stress platform with a network camera, which can monitor the BCS of dairy cow remotely, and the back-view images of the cows were collected and the data set labeled by veterinary experts was built. Secondly, the improved Sing Shot multi-box Detector (SSD) algorithm was introduced to assess the BCS of each image. Finally, the experiments were carried out and the results showed the improved SSD had advantages of higher detecting speed and smaller model size compared with the original SSD. Abstract Body condition scores (BCS) is an important parameter, which is in high correlation with the health status of a dairy cow, metabolic disorder and milk composition during the production period. To evaluate BCS, the traditional methods rely on veterinary experts or skilled staff to look at a cow and touch it. These methods have low efficiency especially on large-scale farms. Computer vision methods are widely used but there are some improvements to increase BCS accuracy. In this study, a low cost BCS evaluation method based on deep learning and machine vision is proposed. Firstly, the back-view images of the cows are captured by network cameras, resulting in 8972 images that constituted the sample data set. The camera is a common 2D camera, which is cheaper and easier to install compared with 3D cameras. Secondly, the key body parts such as tails, pins and rump in the images were labeled manually, the Sing Shot multi-box Detector (SSD) method was used to detect the tail and evaluate the BCS. Inspired by DenseNet and Inception-v4, a new SSD was introduced by changing the network connection method of the original SSD. Finally, the experiments show that the improved SSD method can achieve 98.46% classification accuracy and 89.63% location accuracy, and it has: (1) faster detection speed with 115 fps; (2) smaller model size with 23.1 MB compared to original SSD and YOLO-v3, these are significant advantages for reducing hardware costs.
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Affiliation(s)
- Xiaoping Huang
- Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China
- University of Science and Technology of China, Hefei 230026, China
| | - Zelin Hu
- University of Science and Technology of China, Hefei 230026, China
| | - Xiaorun Wang
- School of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
| | - Xuanjiang Yang
- Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China
- University of Science and Technology of China, Hefei 230026, China
| | - Jian Zhang
- Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China.
| | - Daoling Shi
- School of Electronic and Communication Engineering, Anhui Xinhua University, Hefei 230088, China
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Song X, Bokkers EAM, van Mourik S, Groot Koerkamp PWG, van der Tol PPJ. Automated body condition scoring of dairy cows using 3-dimensional feature extraction from multiple body regions. J Dairy Sci 2019; 102:4294-4308. [PMID: 30879819 DOI: 10.3168/jds.2018-15238] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 01/28/2019] [Indexed: 11/19/2022]
Abstract
Machine vision technology has been used in automated body condition score (BCS) classification of dairy cows. The current vision-based classifications use information acquired from a limited number of body regions of the cow. Our study aimed to improve automated BCS classification by including multiple body condition-related features extracted from 3 viewpoints in 8 body regions. The data set of this study included 44 lactating cows with their BCS evenly distributed over the scale of BCS from 1.5 to 4.5 units. The body images of these cows were recorded over 2 consecutive days using 3-dimensional cameras positioned to view the cow from the top, right side, and rear. Each image was automatically processed to identify anatomical landmarks on the body surface. Around these anatomical landmarks, the bony prominences and body surface depressions were quantified to describe 8 body condition-related features. A manual BCS of each cow was independently assigned by 2 trained assessors using the same predefined protocol. With the extracted features as inputs and manual BCS as the reference, we built a nearest-neighbor classification model to classify BCS and obtained an overall classification sensitivity of 0.72 using a 10-fold cross-validation. We conclude that the sensitivity of automated BCS classification has been improved by expanding the selection of body condition-related features extracted from multiple body regions.
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Affiliation(s)
- X Song
- Farm Technology Group, Wageningen University & Research, PO Box 16, Wageningen, 6700 AA, the Netherlands; Sensors and Data Analysis Department, Lely Innovation, Cornelis van der Lelylaan 1, Maassluis, 3147 PB, the Netherlands.
| | - E A M Bokkers
- Animal Production Systems Group, Wageningen University & Research, PO Box 338, Wageningen, 6700 AH, the Netherlands
| | - S van Mourik
- Farm Technology Group, Wageningen University & Research, PO Box 16, Wageningen, 6700 AA, the Netherlands
| | - P W G Groot Koerkamp
- Farm Technology Group, Wageningen University & Research, PO Box 16, Wageningen, 6700 AA, the Netherlands
| | - P P J van der Tol
- Farm Technology Group, Wageningen University & Research, PO Box 16, Wageningen, 6700 AA, the Netherlands; Sensors and Data Analysis Department, Lely Innovation, Cornelis van der Lelylaan 1, Maassluis, 3147 PB, the Netherlands
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Tullo E, Finzi A, Guarino M. Review: Environmental impact of livestock farming and Precision Livestock Farming as a mitigation strategy. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 650:2751-2760. [PMID: 30373053 DOI: 10.1016/j.scitotenv.2018.10.018] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 10/01/2018] [Accepted: 10/02/2018] [Indexed: 05/22/2023]
Abstract
This paper reviews the environmental impact of current livestock practices and discusses the advantages offered by Precision Livestock Farming (PLF), as a potential strategy to mitigate environmental risks. PLF is defined as: "the application of process engineering principles and techniques to livestock farming to automatically monitor, model and manage animal production". The primary goal of PLF is to make livestock farming more economically, socially and environmentally sustainable and this can be obtained through the observation, interpretation of behaviours and, if possible, individual control of animals. Furthermore, adopting PLF to support management strategies, may lead to the reduction of the environmental impact of farms. Currently, few studies reported PLF efficacy in reducing the environmental impact, however further studies are necessary to better analyze the actual potential of PLF as a mitigation strategy. Literature shows the potentiality of the application of PLF, as the introduction of PLF in farms can lead to a reduction of Greenhouse gases (GHG) and ammonia (NH3) emission in air, nitrates and antibiotics pollution in water bodies, phosphorus, antibiotics and heavy metals in the soil.
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Affiliation(s)
- Emanuela Tullo
- Department of Science and Environmental Policy, Università degli Studi di Milano, Via Celoria 2, 20133 Milano, Italy.
| | - Alberto Finzi
- Department of Agricultural and Environmental Sciences - Production, Landscape, Agroenergy, Università degli Studi di Milano, Via Celoria 2, 20133 Milano, Italy
| | - Marcella Guarino
- Department of Science and Environmental Policy, Università degli Studi di Milano, Via Celoria 2, 20133 Milano, Italy
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Coughlan NE, Cuthbert C, O Sullivan C, McSweeney D. Letter to the Editor: Evidence-based farriery - does it exist? Equine Vet J 2018; 51:136-137. [PMID: 30387867 DOI: 10.1111/evj.13023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- N E Coughlan
- Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Medical Biology Centre, Belfast, Northern Ireland, UK
| | - C Cuthbert
- Centre for Equine and Animal Science (CEQAS), Writtle University College, Chelmsford, Essex, UK
| | - C O Sullivan
- Animal & Grassland Research and Innovation Centre, Cork, Ireland
| | - D McSweeney
- Animal & Grassland Research and Innovation Centre, Cork, Ireland
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