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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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2
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Zhou H, Li Q, Xie Q. Individual Pig Identification Using Back Surface Point Clouds in 3D Vision. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115156. [PMID: 37299883 DOI: 10.3390/s23115156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 05/16/2023] [Accepted: 05/21/2023] [Indexed: 06/12/2023]
Abstract
The individual identification of pigs is the basis for precision livestock farming (PLF), which can provide prerequisites for personalized feeding, disease monitoring, growth condition monitoring and behavior identification. Pig face recognition has the problem that pig face samples are difficult to collect and images are easily affected by the environment and body dirt. Due to this problem, we proposed a method for individual pig identification using three-dimension (3D) point clouds of the pig's back surface. Firstly, a point cloud segmentation model based on the PointNet++ algorithm is established to segment the pig's back point clouds from the complex background and use it as the input for individual recognition. Then, an individual pig recognition model based on the improved PointNet++LGG algorithm was constructed by increasing the adaptive global sampling radius, deepening the network structure and increasing the number of features to extract higher-dimensional features for accurate recognition of different individuals with similar body sizes. In total, 10,574 3D point cloud images of ten pigs were collected to construct the dataset. The experimental results showed that the accuracy of the individual pig identification model based on the PointNet++LGG algorithm reached 95.26%, which was 2.18%, 16.76% and 17.19% higher compared with the PointNet model, PointNet++SSG model and MSG model, respectively. Individual pig identification based on 3D point clouds of the back surface is effective. This approach is easy to integrate with functions such as body condition assessment and behavior recognition, and is conducive to the development of precision livestock farming.
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Affiliation(s)
- Hong Zhou
- College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
| | - Qingda Li
- College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
| | - Qiuju Xie
- College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China
- Key Laboratory of Swine Facilities Engineering, Ministry of Agriculture, Harbin 150030, China
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3
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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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4
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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: 6] [Impact Index Per Article: 6.0] [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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Menezes GL, Bresolin T, Halfman W, Sterry R, Cauffman A, Stuttgen S, Schlesser H, Nelson MA, Bjurstrom A, Rosa GJM, Dorea JRR. Exploring associations among morphometric measurements, genetic group of sire, and performance of beef on dairy calves. Transl Anim Sci 2023; 7:txad064. [PMID: 37601954 PMCID: PMC10433787 DOI: 10.1093/tas/txad064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 06/15/2023] [Indexed: 08/22/2023] Open
Abstract
Sire selection for beef on dairy crosses plays an important role in livestock systems as it may affect future performance and carcass traits of growing and finishing crossbred cattle. The phenotypic variation found in beef on dairy crosses has raised concerns from meat packers due to animals with dairy-type carcass characteristics. The use of morphometric measurements may help to understand the phenotypic structures of sire progeny for selecting animals with greater performance. In addition, due to the relationship with growth, these measurements could be used to early predict the performance until the transition from dairy farms to sales. The objectives of this study were 1) to evaluate the effect of different beef sires and breeds on the morphometric measurements of crossbred calves including cannon bone (CB), forearm (FA), hip height (HH), face length (FL), face width (FW) and growth performance; and (2) to predict the weight gain from birth to transition from dairy farms to sale (WG) and the body weight at sale (BW) using such morphometric measurements obtained at first days of animals' life. CB, FA, HH, FL, FW, and weight at 7 ± 5 d (BW7) (Table 1) were measured on 206 calves, from four different sire breeds [Angus (AN), SimAngus (SA), Simmental (SI), and Limousin (LI)], from five farms. To evaluate the morphometric measurements at the transition from dairy farms to sale and animal performance 91 out of 206 calves sourced from four farms, and offspring of two different sires (AN and SA) were used. To predict the WG and BW, 97 calves, and offspring of three different sires (AN, SA, and LI) were used. The data were analyzed using a mixed model, considering farm and sire as random effects. To predict WG and BW, two linear models (including or not the morphometric measurements) were used, and a leave-one-out cross-validation strategy was used to evaluate their predictive quality. The HH and BW7 were 7.67% and 10.7% higher (P < 0.05) in SA crossbred calves compared to AN, respectively. However, the ADG and adjusted body weight to 120 d were 14.3% and 9.46% greater (P < 0.05) in AN compared to SA. The morphometric measurements improved the model's predictive performance for WG and BW. In conclusion, morphometric measurements at the first days of calves' life can be used to predict animals' performance in beef on dairy. Such a strategy could lead to optimized management decisions and greater profitability in dairy farms.
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Affiliation(s)
- Guilherme L Menezes
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
- Department of Animal Science, Federal University of Minas Gerais, Belo Horizonte, MG 31270-901, Brazil
| | - Tiago Bresolin
- Department of Animal Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - William Halfman
- Division of Extension, University of Wisconsin-Madison Extension, Madison, WI 53706, USA
| | - Ryan Sterry
- Division of Extension, University of Wisconsin-Madison Extension, Madison, WI 53706, USA
| | - Amanda Cauffman
- Division of Extension, University of Wisconsin-Madison Extension, Madison, WI 53706, USA
| | - Sandy Stuttgen
- Division of Extension, University of Wisconsin-Madison Extension, Madison, WI 53706, USA
| | - Heather Schlesser
- Division of Extension, University of Wisconsin-Madison Extension, Madison, WI 53706, USA
| | - Megan A Nelson
- Division of Extension, University of Wisconsin-Madison Extension, Madison, WI 53706, USA
| | - Aerica Bjurstrom
- Division of Extension, University of Wisconsin-Madison Extension, Madison, WI 53706, USA
| | - Guilherme J M Rosa
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Joao R R Dorea
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
- Department of Biological Systems Engineering, Madison, WI 53706, USA
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6
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Xiong Y, Condotta ICFS, Musgrave JA, Brown-Brandl TM, Mulliniks JT. Estimating body weight and body condition score of mature beef cows using depth images. Transl Anim Sci 2023; 7:txad085. [PMID: 37583486 PMCID: PMC10424719 DOI: 10.1093/tas/txad085] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/21/2023] [Indexed: 08/17/2023] Open
Abstract
Obtaining accurate body weight (BW) is crucial for management decisions yet can be a challenge for cow-calf producers. Fast-evolving technologies such as depth sensing have been identified as low-cost sensors for agricultural applications but have not been widely validated for U.S. beef cattle. This study aimed to (1) estimate the body volume of mature beef cows from depth images, (2) quantify BW and metabolic weight (MBW) from image-projected body volume, and (3) classify body condition scores (BCS) from image-obtained measurements using a machine-learning-based approach. Fifty-eight crossbred cows with a mean BW of 410.0 ± 60.3 kg and were between 4 and 6 yr of age were used for data collection between May and December 2021. A low-cost, commercially available depth sensor was used to collect top-view depth images. Images were processed to obtain cattle biometric measurements, including MBW, body length, average height, maximum body width, dorsal area, and projected body volume. The dataset was partitioned into training and testing datasets using an 80%:20% ratio. Using the training dataset, linear regression models were developed between image-projected body volume and BW measurements. Results were used to test BW predictions for the testing dataset. A machine-learning-based multivariate analysis was performed with 29 algorithms from eight classifiers to classify BCS using multiple inputs conveniently obtained from the cows and the depth images. A feature selection algorithm was performed to rank the relevance of each input to the BCS. Results demonstrated a strong positive correlation between the image-projected cow body volume and the measured BW (r = 0.9166). The regression between the cow body volume and the measured BW had a co-efficient of determination (R2) of 0.83 and a 19.2 ± 13.50 kg mean absolute error (MAE) of prediction. When applying the regression to the testing dataset, an increase in the MAE of the predicted BW (22.7 ± 13.44 kg) but a slightly improved R2 (0.8661) was noted. Among all algorithms, the Bagged Tree model in the Ensemble class had the best performance and was used to classify BCS. Classification results demonstrate the model failed to predict any BCS lower than 4.5, while it accurately classified the BCS with a true prediction rate of 60%, 63.6%, and 50% for BCS between 4.75 and 5, 5.25 and 5.5, and 5.75 and 6, respectively. This study validated using depth imaging to accurately predict BW and classify BCS of U.S. beef cow herds.
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Affiliation(s)
- Yijie Xiong
- Department of Animal Science, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Isabella C F S Condotta
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Jacki A Musgrave
- West Central Research and Extension Center, University of Nebraska-Lincoln, North Platte, NE 69101, USA
| | - Tami M Brown-Brandl
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - J Travis Mulliniks
- Department of Animal Science, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
- West Central Research and Extension Center, University of Nebraska-Lincoln, North Platte, NE 69101, USA
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Kadlec R, Indest S, Castro K, Waqar S, Campos LM, Amorim ST, Bi Y, Hanigan MD, Morota G. Automated acquisition of top-view dairy cow depth image data using an RGB-D sensor camera. Transl Anim Sci 2022; 6:txac163. [PMID: 36601061 PMCID: PMC9801406 DOI: 10.1093/tas/txac163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/07/2022] [Indexed: 12/15/2022] Open
Abstract
Animal dimensions are essential indicators for monitoring their growth rate, diet efficiency, and health status. A computer vision system is a recently emerging precision livestock farming technology that overcomes the previously unresolved challenges pertaining to labor and cost. Depth sensor cameras can be used to estimate the depth or height of an animal, in addition to two-dimensional information. Collecting top-view depth images is common in evaluating body mass or conformational traits in livestock species. However, in the depth image data acquisition process, manual interventions are involved in controlling a camera from a laptop or where detailed steps for automated data collection are not documented. Furthermore, open-source image data acquisition implementations are rarely available. The objective of this study was to 1) investigate the utility of automated top-view dairy cow depth data collection methods using picture- and video-based methods, 2) evaluate the performance of an infrared cut lens, 3) and make the source code available. Both methods can automatically perform animal detection, trigger recording, capture depth data, and terminate recording for individual animals. The picture-based method takes only a predetermined number of images whereas the video-based method uses a sequence of frames as a video. For the picture-based method, we evaluated 3- and 10-picture approaches. The depth sensor camera was mounted 2.75 m above-the-ground over a walk-through scale between the milking parlor and the free-stall barn. A total of 150 Holstein and 100 Jersey cows were evaluated. A pixel location where the depth was monitored was set up as a point of interest. More than 89% of cows were successfully captured using both picture- and video-based methods. The success rates of the picture- and video-based methods further improved to 92% and 98%, respectively, when combined with an infrared cut lens. Although both the picture-based method with 10 pictures and the video-based method yielded accurate results for collecting depth data on cows, the former was more efficient in terms of data storage. The current study demonstrates automated depth data collection frameworks and a Python implementation available to the community, which can help facilitate the deployment of computer vision systems for dairy cows.
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Affiliation(s)
- Robert Kadlec
- Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Sam Indest
- Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Kayla Castro
- Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Shayan Waqar
- Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Leticia M Campos
- School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Sabrina T Amorim
- School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Ye Bi
- School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Mark D Hanigan
- School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
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Brown W, Caputo M, Siberski C, Koltes J, Peñagaricano F, Weigel K, White H. Predicting dry matter intake in mid-lactation Holstein cows using point-in-time data streams available on dairy farms. J Dairy Sci 2022; 105:9666-9681. [DOI: 10.3168/jds.2021-21650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 07/21/2022] [Indexed: 11/17/2022]
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McVey C, Egger D, Pinedo P. Improving the Reliability of Scale-Free Image Morphometrics in Applications with Minimally Restrained Livestock Using Projective Geometry and Unsupervised Machine Learning. SENSORS (BASEL, SWITZERLAND) 2022; 22:8347. [PMID: 36366045 PMCID: PMC9653925 DOI: 10.3390/s22218347] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 10/26/2022] [Accepted: 10/28/2022] [Indexed: 06/16/2023]
Abstract
Advances in neural networks have garnered growing interest in applications of machine vision in livestock management, but simpler landmark-based approaches suitable for small, early stage exploratory studies still represent a critical stepping stone towards these more sophisticated analyses. While such approaches are well-validated for calibrated images, the practical limitations of such imaging systems restrict their applicability in working farm environments. The aim of this study was to validate novel algorithmic approaches to improving the reliability of scale-free image biometrics acquired from uncalibrated images of minimally restrained livestock. Using a database of 551 facial images acquired from 108 dairy cows, we demonstrate that, using a simple geometric projection-based approach to metric extraction, a priori knowledge may be leveraged to produce more intuitive and reliable morphometric measurements than conventional informationally complete Euclidean distance matrix analysis. Where uncontrolled variations in image annotation, camera position, and animal pose could not be fully controlled through the design of morphometrics, we further demonstrate how modern unsupervised machine learning tools may be used to leverage the systematic error structures created by such lurking variables in order to generate bias correction terms that may subsequently be used to improve the reliability of downstream statistical analyses and dimension reduction.
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Affiliation(s)
- Catherine McVey
- Department of Animal Sciences, Colorado State University, Fort Collins, CO 80523, USA
| | - Daniel Egger
- Pratt School of Engineering, Duke University, Durham, NC 27708, USA
| | - Pablo Pinedo
- Department of Animal Sciences, Colorado State University, Fort Collins, CO 80523, USA
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Xavier C, Le Cozler Y, Depuille L, Caillot A, Lebreton A, Allain C, Delouard J, Delattre L, Luginbuhl T, Faverdin P, Fischer A. The use of 3-dimensional imaging of Holstein cows to estimate body weight and monitor the composition of body weight change throughout lactation. J Dairy Sci 2022; 105:4508-4519. [DOI: 10.3168/jds.2021-21337] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 01/06/2022] [Indexed: 11/19/2022]
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Batista CP, Gonçalves RS, Contreras LVQ, Valle SDF, González F. Correlation between liver lipidosis, body condition score variation, and hepatic analytes in dairy cows. BRAZILIAN JOURNAL OF VETERINARY MEDICINE 2022; 44:e005121. [PMID: 35749105 PMCID: PMC9179203 DOI: 10.29374/2527-2179.bjvm005121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/31/2022] [Indexed: 11/05/2022] Open
Abstract
Liver lipidosis is a metabolic disorder mostly observed in high yielding dairy cattle, especially during the transition period. The aim of this study was to determine the correlation between hepatic lipid infiltration, biochemical indicators of liver function, and body condition score (BCS) variation in dairy cows. Fifty-one multiparous Holstein cows raised in a confined system were evaluated. Liver biopsies and blood samples were collected, and BCS was measured on days 3 and 28 postpartum. Lipid infiltration was determined by histologic examination. The plasma activity of aspartate aminotransferase, alkaline phosphatase, and gamma-glutamyl transferase and concentration of beta-hydroxybutyrate, non-esterified fatty acids, albumin, total bilirubin, and cholesterol were determined. BCS was measured using objective (camera) and subjective (visual) methods. Mild lipid infiltration was found in 3.92% of cows sampled on day 3 and 5.88% on day 28. Bilirubin was significantly higher on day 3 than on day 28 postpartum, and cholesterol was significantly higher on day 28 than on day 3 in all cows. There was no difference in biochemical analytes between cows with and without lipidosis. On day 3, mean subjective BCS was 3.10 and objective BCS was 3.16, while on day 28, these scores were 2.91 and 2.99, respectively. The calculated liver function index (LFI) was found to be a more sensitive indicator of liver function than the hepatic analytes evaluated. No correlation between BCS variation and lipid infiltration was found. Cholesterol and bilirubin levels showed the most remarkable changes during the early postpartum period. LFI is a potential indicator of postpartum liver function.
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Affiliation(s)
- Chester Patrique Batista
- Veterinarian, MSc. Programa de Pós-Graduação em Ciências Veterinárias (PPGCV), Departamento de Patologia Clínica Veterinária (DPCV), Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil.
| | - Rodrigo Schallenberger Gonçalves
- Veterinarian, MSc. Programa de Pós-Graduação em Ciências Veterinárias (PPGCV), Departamento de Patologia Clínica Veterinária (DPCV), Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil.
| | - Laura Victoria Quishpe Contreras
- Veterinarian, PPGCV, DPCV, UFRGS, Porto Alegre, RS, Brazil.
- Correspondence Laura Victoria Quishpe Contreras Departamento de Patologia Clínica Veterinária, Universidade Federal do Rio Grande do Sul - UFRGS Av. Bento Gonçalves, 9090 CEP 91540-000 - Porto Alegre (RS), Brasil E-mail:
| | | | - Félix González
- Veterinarian, DSc. PPGCV, DPCV, UFRG, Porto Alegre, RS, Brazil.
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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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Williams M, Murphy CP, Sleator RD, Ring SC, Berry DP. Are subjectively scored linear type traits suitable predictors of the genetic merit for feed intake in grazing Holstein-Friesian dairy cows? J Dairy Sci 2021; 105:1346-1356. [PMID: 34955265 DOI: 10.3168/jds.2021-20922] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 10/18/2021] [Indexed: 11/19/2022]
Abstract
Measuring dry matter intake (DMI) in grazing dairy cows using currently available techniques is invasive, time consuming, and expensive. An alternative to directly measuring DMI for use in genetic evaluations is to identify a set of readily available animal features that can be used in a multitrait genetic evaluation for DMI. The objectives of the present study were thus to estimate the genetic correlations between readily available body-related linear type traits and DMI in grazing lactating Holstein-Friesian cows, but importantly also estimate the partial genetic correlations between these linear traits and DMI, after adjusting for differences in genetic merit for body weight. Also of interest was whether the predictive ability derived from the estimated genetic correlations materialized upon validation. After edits, a total of 8,055 test-day records of DMI, body weight, and milk yield from 1,331 Holstein-Friesian cows were available, as were chest width, body depth, and stature from 47,141 first lactation Holstein-Friesian cows. In addition to considering the routinely recorded linear type traits individually, novel composite traits were defined as the product of the linear type traits as an approximation of rumen volume. All linear type traits were moderately heritable, with heritability estimates ranging from 0.27 (standard error = 0.14) to 0.49 (standard error = 0.15); furthermore, all linear type traits were genetically correlated (0.29 to 0.63, standard error 0.14 to 0.12) with DMI. The genetic correlations between the individual linear type traits and DMI, when adjusted for genetic differences in body weight, varied from -0.51 (stature) to 0.48 (chest width). These genetic correlations between DMI and linear type traits suggest linear type traits may be useful predictors of DMI, even when body weight information is available. Nonetheless, estimated genetic merit of DMI derived from a multitrait genetic evaluation of linear type traits did not correlate strongly with actual DMI in a set of validation animals; the benefit was even less if body weight data were also available.
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Affiliation(s)
- M Williams
- Department of Animal Bioscience, Animal and Grassland Research and Innovation Centre, Teagasc, Moorepark, Fermoy, Co. Cork, Ireland P61 C996; Department of Biological Sciences, Munster Technological University, Bishopstown, Co. Cork, Ireland T12 P928
| | - C P Murphy
- Department of Biological Sciences, Munster Technological University, Bishopstown, Co. Cork, Ireland T12 P928
| | - R D Sleator
- Department of Biological Sciences, Munster Technological University, Bishopstown, Co. Cork, Ireland T12 P928
| | - S C Ring
- Irish Cattle Breeding Federation, Highfield House, Bandon, Co. Cork, Ireland P72 X050
| | - D P Berry
- Department of Animal Bioscience, Animal and Grassland Research and Innovation Centre, Teagasc, Moorepark, Fermoy, Co. Cork, Ireland P61 C996.
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Siberski-Cooper CJ, Koltes JE. Opportunities to Harness High-Throughput and Novel Sensing Phenotypes to Improve Feed Efficiency in Dairy Cattle. Animals (Basel) 2021; 12:ani12010015. [PMID: 35011121 PMCID: PMC8749788 DOI: 10.3390/ani12010015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/13/2021] [Accepted: 12/15/2021] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Sensors, routinely collected on-farm tests, and other repeatable, high-throughput measurements can provide novel phenotype information on a frequent basis. Information from these sensors and high-throughput measurements could be harnessed to monitor or predict individual dairy cow feed intake. Predictive algorithms would allow for genetic selection of animals that consume less feed while producing the same amount of milk. Improved monitoring of feed intake could reduce the cost of milk production, improve animal health, and reduce the environmental impact of the dairy industry. Moreover, data from these information sources could aid in animal management (e.g., precision feeding and health detection). In order to implement tools, the relationship of measurements with feed intake needs to be established and prediction equations developed. Lastly, consideration should be given to the frequency of data collection, the need for standardization of data and other potential limitations of tools in the prediction of feed intake. This review summarizes measurements of feed efficiency, factors that may impact the efficiency and feed consumption of an animal, tools that have been researched and new traits that could be utilized for the prediction of feed intake and efficiency, and prediction equations for feed intake and efficiency presented in the literature to date. Abstract Feed for dairy cattle has a major impact on profitability and the environmental impact of farms. Sustainable dairy production relies on continued improvement in feed efficiency as a way to reduce costs and nutrient loss from feed. Advances in breeding, feeding and management have led to the dilution of maintenance energy and thus more efficient dairy cattle. Still, many additional opportunities are available to improve individual animal feed efficiency. Sensing technologies such as wearable sensors, image-based and high-throughput phenotyping technologies (e.g., milk testing) are becoming more available on commercial farm. The application of these technologies as indicator traits for feed intake and efficiency related traits would be advantageous to provide additional information to predict and manage feed efficiency. This review focuses on precision livestock technologies and high-throughput phenotyping in use today as well as those that could be developed in the future as possible indicators of feed intake. Several technologies such as milk spectral data, activity, rumen measures, and image-based phenotypes have been associated with feed intake. Future applications will depend on the ability to repeatably measure and calibrate these data across locations, so that they can be integrated for use in predicting and managing feed intake and efficiency on farm.
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Tedde A, Grelet C, Ho PN, Pryce JE, Hailemariam D, Wang Z, Plastow G, Gengler N, Brostaux Y, Froidmont E, Dehareng F, Bertozzi C, Crowe MA, Dufrasne I, Soyeurt H. Validation of Dairy Cow Bodyweight Prediction Using Traits Easily Recorded by Dairy Herd Improvement Organizations and Its Potential Improvement Using Feature Selection Algorithms. Animals (Basel) 2021; 11:1288. [PMID: 33946238 PMCID: PMC8145206 DOI: 10.3390/ani11051288] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/27/2021] [Accepted: 04/28/2021] [Indexed: 01/22/2023] Open
Abstract
Knowing the body weight (BW) of a cow at a specific moment or measuring its changes through time is of interest for management purposes. The current work aimed to validate the feasibility of predicting BW using the day in milk, parity, milk yield, and milk mid-infrared (MIR) spectrum from a multiple-country dataset and reduce the number of predictors to limit the risk of over-fitting and potentially improve its accuracy. The BW modeling procedure involved feature selections and herd-independent validation in identifying the most interesting subsets of predictors and then external validation of the models. From 1849 records collected in 9 herds from 360 Holstein cows, the best performing models achieved a root mean square error (RMSE) for the herd-independent validation between 52 ± 2.34 kg to 56 ± 3.16 kg, including from 5 to 62 predictors. Among these models, three performed remarkably well in external validation using an independent dataset (N = 4067), resulting in RMSE ranging from 52 to 56 kg. The results suggest that multiple optimal BW predictive models coexist due to the high correlations between adjacent spectral points.
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Affiliation(s)
- Anthony Tedde
- AGROBIOCHEM Department, Research and Teaching Centre (TERRA), Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium; (N.G.); (Y.B.); (H.S.)
- National Funds for Scientific Research, 1000 Brussels, Belgium
| | - Clément Grelet
- Walloon Agricultural Research Center (CRA-W), 5030 Gembloux, Belgium; (C.G.); (E.F.); (F.D.)
| | - Phuong N. Ho
- Agriculture Victoria Research, Centre for AgriBioscience, AgriBio, Bundoora, VIC 3083, Australia; (P.N.H.); (J.E.P.)
| | - Jennie E. Pryce
- Agriculture Victoria Research, Centre for AgriBioscience, AgriBio, Bundoora, VIC 3083, Australia; (P.N.H.); (J.E.P.)
- School of Applied Systems Biology, La Trobe University, 5 Ring Road, Bundoora, VIC 3083, Australia
| | - Dagnachew Hailemariam
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada; (D.H.); (Z.W.); (G.P.)
| | - Zhiquan Wang
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada; (D.H.); (Z.W.); (G.P.)
| | - Graham Plastow
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada; (D.H.); (Z.W.); (G.P.)
| | - Nicolas Gengler
- AGROBIOCHEM Department, Research and Teaching Centre (TERRA), Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium; (N.G.); (Y.B.); (H.S.)
| | - Yves Brostaux
- AGROBIOCHEM Department, Research and Teaching Centre (TERRA), Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium; (N.G.); (Y.B.); (H.S.)
| | - Eric Froidmont
- Walloon Agricultural Research Center (CRA-W), 5030 Gembloux, Belgium; (C.G.); (E.F.); (F.D.)
| | - Frédéric Dehareng
- Walloon Agricultural Research Center (CRA-W), 5030 Gembloux, Belgium; (C.G.); (E.F.); (F.D.)
| | | | - Mark A. Crowe
- UCD School of Veterinary Medicine, University College Dublin, D04 V1W8 Dublin, Ireland;
| | - Isabelle Dufrasne
- Faculty of Veterinary Medicine, University of Liège, Quartier Vallée 2, 4000 Liège, Belgium;
| | | | - Hélène Soyeurt
- AGROBIOCHEM Department, Research and Teaching Centre (TERRA), Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium; (N.G.); (Y.B.); (H.S.)
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16
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Martin MJ, Dórea JRR, Borchers MR, Wallace RL, Bertics SJ, DeNise SK, Weigel KA, White HM. Comparison of methods to predict feed intake and residual feed intake using behavioral and metabolite data in addition to classical performance variables. J Dairy Sci 2021; 104:8765-8782. [PMID: 33896643 DOI: 10.3168/jds.2020-20051] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 03/13/2021] [Indexed: 01/23/2023]
Abstract
Predicting dry matter intake (DMI) and feed efficiency by leveraging the use of data streams available on farm could aid efforts to improve the feed efficiency of dairy cattle. Residual feed intake (RFI) is the difference between predicted and observed feed intake after accounting for body size, body weight change, and milk production, making it a valuable metric for feed efficiency research. Our objective was to develop and evaluate DMI and RFI prediction models using multiple linear regression (MLR), partial least squares regression, artificial neural networks, and stacked ensembles using different combinations of cow descriptive, performance, sensor-derived behavioral (SMARTBOW; Zoetis), and blood metabolite data. Data were collected from mid-lactation Holstein cows (n = 124; 102 multiparous, 22 primiparous) split equally between 2 replicates of 45-d duration with ad libitum access to feed. Within each predictive approach, 4 data streams were added in sequence: dataset M (week of lactation, parity, milk yield, and milk components), dataset MB (dataset M plus body condition score and metabolic body weight), dataset MBS (dataset MB plus sensor-derived behavioral variables), and dataset MBSP (dataset MBS plus physiological blood metabolites). The combination of 4 datasets and 4 analytical approaches resulted in 16 analyses of DMI and RFI, using variables averaged within cow across the study period. Additional models using weekly averaged data within cow and study were built using all predictive approaches for datasets M, MB, and MBS. Model performance was assessed using the coefficient of determination, concordance correlation coefficient, and root mean square error of prediction. Predictive models of DMI performed similarly across all approaches, and models using dataset MBS had the greatest model performance. The best approach-dataset combination was MLR-dataset MBS, although several models performed similarly. Weekly DMI models had the greatest performance with MLR and partial least squares regression approaches. Dataset MBS models had incrementally better performance than datasets MB and M. Within each approach-dataset combination, models with DMI averaged over the study period had slightly greater model performance than DMI averaged weekly. Predictive performance of all RFI models was poor, but slight improvements when using MLR applied to dataset MBS suggest that rumination and activity behaviors may explain some of the variation in RFI. Overall, similar performance of MLR, compared with machine learning techniques, indicates MLR may be sufficient to predict DMI. The improvement in model performance with each additional data stream supports the idea of integrating data streams to improve model predictions and farm management decisions.
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Affiliation(s)
- Malia J Martin
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706
| | - J R R Dórea
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706
| | | | | | - S J Bertics
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706
| | | | - K A Weigel
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706
| | - H M White
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706.
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Mardhati M, González LA, Thomson PC, Clark CEF, García SC. Short-term liveweight changes of dairy cows measured by stationary and walk-over weighing scales. J Dairy Sci 2021; 104:8202-8213. [PMID: 33865596 DOI: 10.3168/jds.2020-19912] [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: 11/13/2020] [Accepted: 03/08/2021] [Indexed: 11/19/2022]
Abstract
Monitoring and detecting individual cows' liveweight (LW) and liveweight change (LWC) are important for estimation of nutritional requirements and health management, and could be useful to measure short-term feed intake, water consumption, defecation, and urination. Walk-over weighing (WOW) systems can facilitate measurements of LW for these purposes, providing automated LW recorded at different times of the day. We conducted a field study to (1) quantify the contribution of feed and water intake, as well as urine and feces excretions, to short-term LWC and (2) determine the feasibility of stationary and WOW scales to detect subtle changes in LW as a result of feed and water intake, urination, and defecation. In this experiment, 10 cows walked through a WOW system and then stood individually on a stationary scale collecting weights at 10 and 3.3 Hz, respectively. Cows were offered 4 kg of feed and 10 kg of water on the stationary scale. For each animal, LW before and after eating and drinking was then calculated using different approaches. Liveweight change was calculated as the difference between the initial and final LW before and after eating and drinking for each statistical measure. The weights of feed intake, water consumption, urination, and defecation were measured and used as predictors of LWC. Urine and feces were collected from individual cows while the cow was on the scale, using a container, and weighed separately. The agreement between LWC measured using either stationary or WOW scales was assessed to determine the sensitivity of the scales to detect subtle changes in LW using the coefficient of determination (R2), Lin's concordance correlation coefficient (CCC), and mean bias. The prediction model showed that most of the regression coefficients were not significantly different from +1.0 for feed and water, or -1.0 for urine and feces. The R2 and CCC values demonstrated a satisfactory agreement between calculated and stationary LWC and values ranged from 0.60 to 0.92 and 0.71 to 0.94, respectively. A moderate agreement was achieved between calculated and automated LWC with R2 and Lin's CCC values of 0.45 to 0.63 and 0.60 to 0.74, respectively. Therefore, results demonstrated that new algorithms and data processing methods need to be continuously explored and improved to obtain accurate measurements of LW to measure changes in LW, especially from WOW scales.
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Affiliation(s)
- M Mardhati
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Camden, NSW 2570, Australia; Malaysian Agricultural Research and Development Institute (MARDI), Serdang, 43400 Selangor, Malaysia; Sydney Institute of Agriculture, The University of Sydney, NSW 2006, Australia.
| | - Luciano A González
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Camden, NSW 2570, Australia; Sydney Institute of Agriculture, The University of Sydney, NSW 2006, Australia
| | - Peter C Thomson
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Camden, NSW 2570, Australia; Sydney Institute of Agriculture, The University of Sydney, NSW 2006, Australia
| | - Cameron E F Clark
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Camden, NSW 2570, Australia; Sydney Institute of Agriculture, The University of Sydney, NSW 2006, Australia
| | - Sergio C García
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Camden, NSW 2570, Australia; Sydney Institute of Agriculture, The University of Sydney, NSW 2006, Australia
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18
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Wang Z, Shadpour S, Chan E, Rotondo V, Wood KM, Tulpan D. ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images. J Anim Sci 2021; 99:6149204. [PMID: 33626149 PMCID: PMC7904040 DOI: 10.1093/jas/skab022] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 01/25/2021] [Indexed: 01/01/2023] Open
Abstract
Monitoring, recording, and predicting livestock body weight (BW) allows for timely intervention in diets and health, greater efficiency in genetic selection, and identification of optimal times to market animals because animals that have already reached the point of slaughter represent a burden for the feedlot. There are currently two main approaches (direct and indirect) to measure the BW in livestock. Direct approaches include partial-weight or full-weight industrial scales placed in designated locations on large farms that measure passively or dynamically the weight of livestock. While these devices are very accurate, their acquisition, intended purpose and operation size, repeated calibration and maintenance costs associated with their placement in high-temperature variability, and corrosive environments are significant and beyond the affordability and sustainability limits of small and medium size farms and even of commercial operators. As a more affordable alternative to direct weighing approaches, indirect approaches have been developed based on observed or inferred relationships between biometric and morphometric measurements of livestock and their BW. Initial indirect approaches involved manual measurements of animals using measuring tapes and tubes and the use of regression equations able to correlate such measurements with BW. While such approaches have good BW prediction accuracies, they are time consuming, require trained and skilled farm laborers, and can be stressful for both animals and handlers especially when repeated daily. With the concomitant advancement of contactless electro-optical sensors (e.g., 2D, 3D, infrared cameras), computer vision (CV) technologies, and artificial intelligence fields such as machine learning (ML) and deep learning (DL), 2D and 3D images have started to be used as biometric and morphometric proxies for BW estimations. This manuscript provides a review of CV-based and ML/DL-based BW prediction methods and discusses their strengths, weaknesses, and industry applicability potential.
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Affiliation(s)
- Zhuoyi Wang
- Department of Animal Biosciences, Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, Ontario, Canada
| | - Saeed Shadpour
- Department of Animal Biosciences, Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, Ontario, Canada
| | - Esther Chan
- Department of Animal Biosciences, Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, Ontario, Canada
| | - Vanessa Rotondo
- Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada
| | - Katharine M Wood
- Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada
| | - Dan Tulpan
- Department of Animal Biosciences, Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, Ontario, Canada
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19
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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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20
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Martins B, Mendes A, Silva L, Moreira T, Costa J, Rotta P, Chizzotti M, Marcondes M. Estimating body weight, body condition score, and type traits in dairy cows using three dimensional cameras and manual body measurements. Livest Sci 2020. [DOI: 10.1016/j.livsci.2020.104054] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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21
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Nye J, Zingaretti LM, Pérez-Enciso M. Estimating Conformational Traits in Dairy Cattle With DeepAPS: A Two-Step Deep Learning Automated Phenotyping and Segmentation Approach. Front Genet 2020; 11:513. [PMID: 32508888 PMCID: PMC7253626 DOI: 10.3389/fgene.2020.00513] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 04/27/2020] [Indexed: 01/02/2023] Open
Abstract
Assessing conformation features in an accurate and rapid manner remains a challenge in the dairy industry. While recent developments in computer vision has greatly improved automated background removal, these methods have not been fully translated to biological studies. Here, we present a composite method (DeepAPS) that combines two readily available algorithms in order to create a precise mask for an animal image. This method performs accurately when compared with manual classification of proportion of coat color with an adjusted R2 = 0.926. Using the output mask, we are able to automatically extract useful phenotypic information for 14 additional morphological features. Using pedigree and image information from a web catalog (www.semex.com), we estimated high heritabilities (ranging from h2 = 0.18–0.82), indicating that meaningful biological information has been extracted automatically from imaging data. This method can be applied to other datasets and requires only a minimal number of image annotations (∼50) to train this partially supervised machine-learning approach. DeepAPS allows for the rapid and accurate quantification of multiple phenotypic measurements while minimizing study cost. The pipeline is available at https://github.com/lauzingaretti/deepaps.
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Affiliation(s)
- Jessica Nye
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB Consortium, Barcelona, Spain
| | - Laura M Zingaretti
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB Consortium, Barcelona, Spain
| | - Miguel Pérez-Enciso
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB Consortium, Barcelona, Spain.,ICREA, Barcelona, Spain
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Zhang L, Gengler N, Dehareng F, Colinet F, Froidmont E, Soyeurt H. Can We Observe Expected Behaviors at Large and Individual Scales for Feed Efficiency-Related Traits Predicted Partly from Milk Mid-Infrared Spectra? Animals (Basel) 2020; 10:E873. [PMID: 32443421 PMCID: PMC7278466 DOI: 10.3390/ani10050873] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 05/14/2020] [Accepted: 05/15/2020] [Indexed: 11/24/2022] Open
Abstract
Phenotypes related to feed efficiency were predicted from records easily acquired by breeding organizations. A total of 461,036 and 354,148 records were collected from the first and second parity Holstein cows. Equations were applied to the milk mid-infrared spectra to predict the main milk components and coupled with animal characteristics to predict the body weight (pBW). Dry matter intake (pDMI) was predicted from pBW using the National Research Council (NRC) equation. The consumption index (pIC) was estimated from pDMI and fat, and protein corrected milk. All traits were modeled using single trait test-day models. Descriptive statistics were within the expected range. Milk yield, pDMI, and pBW were phenotypically positively related (r ranged from 0.08 to 0.64). As expected, pIC was phenotypically negatively correlated with milk yield (-0.77 and -0.80 for the first and second lactation) and slightly positively correlated with pBW (0.16 and 0.07 for the first and second lactation). Later, parity cows seemed to have a better feed efficiency as they had a lower pIC. Although the prediction accuracy was moderate, the observed behaviors of studied traits by year, stage of lactation, and parity were in agreement with the literature. Moreover, as a genetic component was highlighted (heritability around 0.18), it would be interesting to realize a genetic evaluation of these traits and compare the obtained breeding values with the ones estimated for sires having daughters with reference feed efficiency records.
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Affiliation(s)
- Lei Zhang
- TERRA Research Centre, University of Liège-Gembloux Agro-Bio Tech, 5030 Gembloux, Belgium; (N.G.); (F.C.); (H.S.)
| | - Nicolas Gengler
- TERRA Research Centre, University of Liège-Gembloux Agro-Bio Tech, 5030 Gembloux, Belgium; (N.G.); (F.C.); (H.S.)
| | - Frédéric Dehareng
- Valorisation of Agricultural Products Department, Walloon Agricultural Research Centre, 5030 Gembloux, Belgium; (F.D.); (E.F.)
| | - Frédéric Colinet
- TERRA Research Centre, University of Liège-Gembloux Agro-Bio Tech, 5030 Gembloux, Belgium; (N.G.); (F.C.); (H.S.)
| | - Eric Froidmont
- Valorisation of Agricultural Products Department, Walloon Agricultural Research Centre, 5030 Gembloux, Belgium; (F.D.); (E.F.)
| | - Hélène Soyeurt
- TERRA Research Centre, University of Liège-Gembloux Agro-Bio Tech, 5030 Gembloux, Belgium; (N.G.); (F.C.); (H.S.)
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Abstract
A plethora of sensors and information technologies with applications to the precision nutrition of herbivores have been developed and continue to be developed. The nutritional processes start outside of the animal body with the available feed (quantity and quality) and continue inside it once the feed is consumed, degraded in the gastrointestinal tract and metabolised by organs and tissues. Finally, some nutrients are wasted via urination, defecation and gaseous emissions through breathing and belching whereas remaining nutrients ensure maintenance and production. Nowadays, several processes can be monitored in real-time using new technologies, but although these provide valuable data 'as is', further gains could be obtained using this information as inputs to nutrition simulation models to predict unmeasurable variables in real-time and to forecast outcomes of interest. Data provided by sensors can create synergies with simulation models and this approach has the potential to expand current applications. In addition, data provided by sensors could be used with advanced analytical techniques such as data fusion, optimisation techniques and machine learning to improve their value for applications in precision animal nutrition. The present paper reviews technologies that can monitor different nutritional processes relevant to animal production, profitability, environmental management and welfare. We discussed the model-data fusion approach in which data provided by sensor technologies can be used as input of nutrition simulation models in near-real time to produce more accurate, certain and timely predictions. We also discuss some examples that have taken this model-data fusion approach to complement the capabilities of both models and sensor data, and provided examples such as predicting feed intake and methane emissions. Challenges with automatising the nutritional management of individual animals include monitoring and predicting of the flow of nutrients including nutrient intake, quantity and composition of body growth and milk production, gestation, maintenance and physical activities at the individual animal level. We concluded that the livestock industries are already seeing benefits from the development of sensor and information technologies, and this benefit is expected to grow exponentially soon with the integration of nutrition simulation models and techniques for big data analysis. However, this approach may need re-evaluating or performing new empirical research in both fields of animal nutrition and simulation modelling to accommodate a new type of data provided by the sensor technologies.
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