A robust machine vision system for body measurements of beef calves

Weales, David; Moussa, Medhat; Tarry, Cole

doi:10.1016/j.atech.2021.100024

Cited by 7 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The group of beef cattle were numbered from 1 to 10 in ascending order, from which 24,16,19,26,14,22,20,11,18, and 12 sets of point clouds were collected, respectively, resulting in 182 sets of point clouds. The body dimensions for each animal were also manually measured for comparison.…”

Section: Body Measurement Resultsmentioning

confidence: 99%

“…To measure the beef cattle body dimensions automatically, Xu et al [18] detected the key points of the cattle body from RGB images through the CentreNet network, and extracted the core parameters, including the body length, body oblique length, and wither height, based on the location of key points. Weales et al [19] divided the cattle body area into three equal regions along the direction of the dorsal line, and the widest slice in each region (from the top view) was recorded to extract the core parameters, such as the wither height, chest girth, and heart circumference, whose average errors were within the 1.9-2.3% range. Wang et al [20] proposed a method to automatically identify and measure the heart girth regions from pig point clouds, and the results showed an average error of approximately 7.9%.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Key Region Extraction and Body Dimension Measurement of Beef Cattle Using 3D Point Clouds

et al. 2022

Agriculture

View full text Add to dashboard Cite

Body dimensions are key indicators for the beef cattle fattening and breeding process. On-animal measurement is relatively inefficient, and can induce severe stress responses among beef cattle and pose a risk for operators, thereby impacting the cattle’s growth rate and wellbeing. To address the above issues, a highly efficient and automatic method was developed to measure beef cattle’s body dimensions, including the oblique length, height, width, abdominal girth, and chest girth, based on the reconstructed three-dimensional point cloud data. The horizontal continuous slice sequence of the complete point clouds was first extracted, and the central point of the beef cattle leg region was determined from the span distribution of the point cloud clusters in the targeted slices. Subsequently, the boundary of the beef cattle leg region was identified by the “five-point clustering gradient boundary recognition algorithm” and was then calibrated, followed by the accurate segmentation of the corresponding region. The key regions for body dimension data calculation were further determined by the proposed algorithm, which forms the basis of the scientific calculation of key body dimensions. The influence of different postures of beef cattle on the measurement results was also preliminarily discussed. The results showed that the errors of calculated body dimensions, i.e., the oblique length, height, width, abdominal girth, and chest girth, were 2.3%, 2.8%, 1.6%, 2.8%, and 2.6%, respectively. In the present work, the beef cattle body dimensions could be effectively measured based on the 3D regional features of the point cloud data. The proposed algorithm shows a degree of generalization and robustness that is not affected by different postures of beef cattle. This automatic method can be effectively used to collect reliable phenotype data during the fattening of beef cattle and can be directly integrated into the breeding process.

show abstract

Section: Body Measurement Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Key Region Extraction and Body Dimension Measurement of Beef Cattle Using 3D Point Clouds

et al. 2022

Agriculture

View full text Add to dashboard Cite

show abstract

“…Machine vision was also used to automatically measure body measurements and weight in calves and heifers, combining ML and 3D cameras with time-of-flight [ 179 ] and RGB-D [ 180 ] technology. Very recently, three studies have tried to automatically predict diseases in calves using ML, namely NCD [ 174 ], BRD [ 163 ], and anaplasmosis [ 181 ].…”

Section: Technological Applications To Monitor Calves’ Health and Wel...mentioning

confidence: 99%

Literature Review on Technological Applications to Monitor and Evaluate Calves’ Health and Welfare

Silva

Conceição

Pereira

et al. 2023

Animals

View full text Add to dashboard Cite

Precision livestock farming (PLF) research is rapidly increasing and has improved farmers’ quality of life, animal welfare, and production efficiency. PLF research in dairy calves is still relatively recent but has grown in the last few years. Automatic milk feeding systems (AMFS) and 3D accelerometers have been the most extensively used technologies in dairy calves. However, other technologies have been emerging in dairy calves’ research, such as infrared thermography (IRT), 3D cameras, ruminal bolus, and sound analysis systems, which have not been properly validated and reviewed in the scientific literature. Thus, with this review, we aimed to analyse the state-of-the-art of technological applications in calves, focusing on dairy calves. Most of the research is focused on technology to detect and predict calves’ health problems and monitor pain indicators. Feeding and lying behaviours have sometimes been associated with health and welfare levels. However, a consensus opinion is still unclear since other factors, such as milk allowance, can affect these behaviours differently. Research that employed a multi-technology approach showed better results than research focusing on only a single technique. Integrating and automating different technologies with machine learning algorithms can offer more scientific knowledge and potentially help the farmers improve calves’ health, performance, and welfare, if commercial applications are available, which, from the authors’ knowledge, are not at the moment.

show abstract

“…Hence, the research on non-contact automatic measuring systems for body size of animals such as pigs, cows, and sheep has been widely carried out [5,14,[20][21][22]. Currently, researchers use two or more depth cameras from different angles to collect surface point clouds of large-sized quadruped animals such as live pigs and cows and automatically measure various body sizes such as body length, body height, thoracic circumference, abdominal circumference, and chest depth by fusing complete point clouds [19,21,[23][24][25][26][27][28][29]. There are also studies using deep learning models to position key body points on RGB images and using intrinsic camera parameters to project the detected key points onto the surface of livestock point clouds, thus achieving body size measurement of pigs and cows [30,31].…”

Section: Introductionmentioning

confidence: 99%

Calculating Volume of Pig Point Cloud Based on Improved Poisson Reconstruction

Lin,

Chen,

et al. 2024

Animals

View full text Add to dashboard Cite

Pig point cloud data can be used to digitally reconstruct surface features, calculate pig body volume and estimate pig body weight. Volume, as a pig novel phenotype feature, has the following functions: (a) It can be used to estimate livestock weight based on its high correlation with body weight. (b) The volume proportion of various body parts (such as head, legs, etc.) can be obtained through point cloud segmentation, and the new phenotype information can be utilized for breeding pigs with smaller head volumes and stouter legs. However, as the pig point cloud has an irregular shape and may be partially missing, it is difficult to form a closed loop surface for volume calculation. Considering the better water tightness of Poisson reconstruction, this article adopts an improved Poisson reconstruction algorithm to reconstruct pig body point clouds, making the reconstruction results smoother, more continuous, and more complete. In the present study, standard shape point clouds, a known-volume Stanford rabbit standard model, a measured volume piglet model, and 479 sets of pig point cloud data with known body weight were adopted to confirm the accuracy and reliability of the improved Poisson reconstruction and volume calculation algorithm. Among them, the relative error was 4% in the piglet model volume result. The average absolute error was 2.664 kg in the weight estimation obtained from pig volume by collecting pig point clouds, and the average relative error was 2.478%. Concurrently, it was determined that the correlation coefficient between pig body volume and pig body weight was 0.95.

show abstract

A robust machine vision system for body measurements of beef calves

Cited by 7 publications

References 8 publications

Key Region Extraction and Body Dimension Measurement of Beef Cattle Using 3D Point Clouds

Key Region Extraction and Body Dimension Measurement of Beef Cattle Using 3D Point Clouds

Literature Review on Technological Applications to Monitor and Evaluate Calves’ Health and Welfare

Calculating Volume of Pig Point Cloud Based on Improved Poisson Reconstruction

Contact Info

Product

Resources

About