Bo Jin , Guorui Wang , Jingze Feng , Yongliang Qiao , Zhifeng Yao , Mei Li , Meili Wang
{"title":"PointStack based 3D automatic body measurement for goat phenotypic information acquisition","authors":"Bo Jin , Guorui Wang , Jingze Feng , Yongliang Qiao , Zhifeng Yao , Mei Li , Meili Wang","doi":"10.1016/j.biosystemseng.2024.09.008","DOIUrl":null,"url":null,"abstract":"<div><div>The body size of livestock is an essential phenotypic trait in genetic breeding, gene improvement, health screening, and animal welfare. To develop a non-contact automatic system for measuring goat body traits, we propose a point-cloud segmentation model based on an improved PointStack, which segments the automatically acquired three-dimensional (3D) point-cloud data of goats into different parts, including the head, front legs, hind legs, chest, abdomen, hip, and tail. The segmented point cloud, along with the physiological features of the goat, is then used to locate the corresponding key points for body size measurement. A novel method for key point localisation is proposed that includes coordinate normalisation, retrieval of key clusters, key point adjustment, optimisation of the traveling salesman problem, and edge detection. These methods were designed to reduce discrepancies at crucial points of body features, thereby facilitating the precise computation of the body size parameter in goats. In this work, 326 point clouds representing the upright posture of 55 goats were used for segmentation and body size measurement testing. The proposed segmentation model achieved a mean intersection over union of 89.21% and accuracy of 94.54%, outperforming comparative models. In the body traits measurement experiment, mean absolute percentage errors for body length, body height, chest width, chest girth, hip height, and hip width were recorded as 3.24%, 2.54%, 5.43%, 3.08%, 2.16%, and 4.59%, respectively. In summary, the proposed automated measurement method demonstrates high accuracy, strong robustness, and holds significant potential for widespread application.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"248 ","pages":"Pages 32-46"},"PeriodicalIF":4.4000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biosystems Engineering","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1537511024002101","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
The body size of livestock is an essential phenotypic trait in genetic breeding, gene improvement, health screening, and animal welfare. To develop a non-contact automatic system for measuring goat body traits, we propose a point-cloud segmentation model based on an improved PointStack, which segments the automatically acquired three-dimensional (3D) point-cloud data of goats into different parts, including the head, front legs, hind legs, chest, abdomen, hip, and tail. The segmented point cloud, along with the physiological features of the goat, is then used to locate the corresponding key points for body size measurement. A novel method for key point localisation is proposed that includes coordinate normalisation, retrieval of key clusters, key point adjustment, optimisation of the traveling salesman problem, and edge detection. These methods were designed to reduce discrepancies at crucial points of body features, thereby facilitating the precise computation of the body size parameter in goats. In this work, 326 point clouds representing the upright posture of 55 goats were used for segmentation and body size measurement testing. The proposed segmentation model achieved a mean intersection over union of 89.21% and accuracy of 94.54%, outperforming comparative models. In the body traits measurement experiment, mean absolute percentage errors for body length, body height, chest width, chest girth, hip height, and hip width were recorded as 3.24%, 2.54%, 5.43%, 3.08%, 2.16%, and 4.59%, respectively. In summary, the proposed automated measurement method demonstrates high accuracy, strong robustness, and holds significant potential for widespread application.
期刊介绍:
Biosystems Engineering publishes research in engineering and the physical sciences that represent advances in understanding or modelling of the performance of biological systems for sustainable developments in land use and the environment, agriculture and amenity, bioproduction processes and the food chain. The subject matter of the journal reflects the wide range and interdisciplinary nature of research in engineering for biological systems.