Accurate body measurement of live cattle using three depth cameras and non-rigid 3-D shape recovery

Ruchay, Alexey; Kober, Vitaly; Dorofeev, Konstantin; Колпаков, В. И.; Мирошников, С. А.

doi:10.1016/j.compag.2020.105821

Cited by 73 publications

(24 citation statements)

References 28 publications

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“…The point cloud fusion and 3-D reconstruction of live cattle were carried out using the proposed automated computer vision system [ 33 ] that can generate one accurate 3D model of live cattle based on RGB-D data. Figure 1 shows RGB images, depth maps, and point clouds of the cattle captured by three Kinect cameras.…”

Section: Resultsmentioning

confidence: 99%

“…Measurements of exterior features and meat productivity were made using a system of accurate measurement of the body of live cattle using three-depth chambers and non-rigid three-dimensional shape restoration [ 33 , 34 , 35 ].…”

Section: Methodsmentioning

confidence: 99%

“…The resolutions of RGB images and depth images are 1920 × 1080 and 512 × 424 pixels, respectively. More information can be found in [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

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Genome-Wide Associative Study of Phenotypic Parameters of the 3D Body Model of Aberdeen Angus Cattle with Multiple Depth Cameras

Ruchay

Колпаков

Kosyan

et al. 2022

Animals

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In beef cattle breeding, genome-wide association studies (GWAS) using single nucleotide polymorphisms (SNPs) arrays can reveal many loci of various production traits, such as growth, productivity, and meat quality. With the development of genome sequencing technologies, new opportunities are opening up for more accurate identification of areas associated with these traits. This article aims to develop a novel approach to the lifetime evaluation of cattle by 3-D visualization of economic-biological and genetic features. The purpose of this study was to identify significant variants underlying differences in the qualitative characteristics of meat, using imputed data on the sequence of the entire genome. Samples of biomaterial of young Aberdeen-Angus breed cattle (n = 96) were the material for carrying out genome-wide SNP genotyping. Genotyping was performed using a high-density DNA chip Bovine GPU HD BeadChip (Illumina Inc., San Diego, CA, USA), containing ~150 thousand SNPs. The following indicators were selected as phenotypic features: chest width and chest girth retrieved by 3-D model and meat output on the bones. Correlation analysis showed a reliable positive relationship between chest width and meat output on the bones, which can potentially be used for lifetime evaluation of meat productivity of animals.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Genome-Wide Associative Study of Phenotypic Parameters of the 3D Body Model of Aberdeen Angus Cattle with Multiple Depth Cameras

Ruchay

Колпаков

Kosyan

et al. 2022

Animals

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“…In response to the problems of the fact that the full-view 3D structural morphology of the soybean canopy cannot be obtained from single and dual views, the low efficiency of manual acquisition of plant phenotypes and the high price of high-end devices, three Microsoft Kinect 2.0 cameras were chosen as the experimental equipment for acquiring data in this study because of fast, stable, high-cost performance and high image resolution [22]. In this study, two varieties of soybean, Heihe 49 and Suinong 26, were used as research objects, and the point clouds of the soybean canopy were obtained by photographing several groups of soybean plants at different growth periods.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Reconstruction of Soybean Canopy Based on Multivision Technology for Calculation of Phenotypic Traits

Wang

et al. 2022

Agronomy

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Precise reconstruction of the morphological structure of the soybean canopy and acquisition of plant traits have great theoretical significance and practical value for soybean variety selection, scientific cultivation, and fine management. Since it is difficult to obtain all-around information on living plants with traditional single or binocular machine vision, this paper proposes a three-dimensional (3D) method of reconstructing the soybean canopy for calculation of phenotypic traits based on multivision. First, a multivision acquisition system based on the Kinect sensor was constructed to obtain all-around point cloud data of soybean in three viewpoints, with different fertility stages of soybean as the research object. Second, conditional filtering and K-nearest neighbor filtering (KNN) algorithms were used to preprocess the raw 3D point cloud. The point clouds were matched and fused by the random sample consensus (RANSAC) and iterative closest point (ICP) algorithms to accomplish the 3D reconstruction of the soybean canopy. Finally, the plant height, leafstalk angle and crown width of soybean were calculated based on the 3D reconstruction of soybean canopy. The experimental results showed that the average deviations of the method was 2.84 cm, 4.0866° and 0.0213 m, respectively. The determination coefficients between the calculated values and measured values were 0.984, 0.9195 and 0.9235. The average deviation of the RANSAC + ICP was 0.0323, which was 0.0214 lower thanthe value calculated by the ICP algorithm. The results enable the precise 3D reconstruction of living soybean plants and quantitative detection for phenotypic traits.

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“…Several studies have explored this idea, applied at different levels of complexity on various livestock species. Ruchay et al [12] manually marked several points in a reconstructed model for non-contact body measurements in cattle, leading to an error of less than 3%. Similarly, Kwon et al [13] proposed an iterative offset-based method to establish a point cloud mesh model of pigs, where the widest regions of the foreleg, hind leg, and middle torso were manually determined, measuring the body dimensions based on geometric features.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

Agriculture

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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.

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Accurate body measurement of live cattle using three depth cameras and non-rigid 3-D shape recovery

Cited by 73 publications

References 28 publications

Genome-Wide Associative Study of Phenotypic Parameters of the 3D Body Model of Aberdeen Angus Cattle with Multiple Depth Cameras

Genome-Wide Associative Study of Phenotypic Parameters of the 3D Body Model of Aberdeen Angus Cattle with Multiple Depth Cameras

Three-Dimensional Reconstruction of Soybean Canopy Based on Multivision Technology for Calculation of Phenotypic Traits

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

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