RESEARCH ON PIG BODY SIZE MEASUREMENT SYSTEM BASED ON STEREO VISION
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Published:2023-08-17
Issue:
Volume:
Page:76-85
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ISSN:2068-2239
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Container-title:INMATEH Agricultural Engineering
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language:en
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Short-container-title:INMATEH
Author:
GENG Yanli1, YUE Xiaodong1, JI Yankai1, LIN Yanbo1, FU Yanfang2, YANG Shucai3
Affiliation:
1. School of Artificial Intelligence, Hebei University of Technology, Tiajin, 300130 / China 2. Hebei Provincial Animal Husbandry Station, Hebei 050035 / China 3. Tianjin Mojieke Technology Development Co., Ltd, Tiajin, 300130 / China
Abstract
Body size of pigs is an important evaluation indicator in pig breeding. The traditional method of body size measurement is usually in manual way, which requires more employees and causes stress reactions of pigs. In response to the shortcomings of the traditional methods, this paper designed a system for measuring the body size of pigs based on stereo vision. The point cloud of both the calibration object and the pig was collected using dual KinectV2 cameras. Pre-processing was conducted using filtering and random sample consensus to remove background noise from the point clouds. As there was limited overlap between the two sides of the point clouds, the rotation matrix obtained from registering the calibration object was applied to the pig point clouds. Curve fitting and slicing were then utilized to measure the pig's body dimensions, including length, width, height, and abdominal circumference. The results of the study indicated that the mean absolute percentage error (MAPE) was 2.13% for body length, 1.02% for body width, 1.05% for body height, and 2.21% for abdominal girth. These results demonstrate the high accuracy and practical production value of the system.
Publisher
INMA Bucharest-Romania
Subject
Industrial and Manufacturing Engineering,Mechanical Engineering,Food Science
Reference22 articles.
1. Apirachai W., Banchar A., Supachai P., (2015). An approach based on digital image analysis to estimate the live weights of pigs in farm environments. Computers and Electronics in Agriculture, Vol.115, pp.26-33, Khon Kaen/ Thailand. 2. Bewley J.M., Peacock A.M., Lewis O., (2008). Potential for estimation of body condition scores in dairy cattle from digital images. Journal of dairy science, Vol.94, Issue 9, pp.3439-3453, West Lafayette/UK. 3. Chen C., Liu T., Wu W., (2018). Application and development of three-dimensional sensor Kinect in agriculture. Jiangsu Agricultural Sciences, Vol.46, Issue 8, pp.11-14, Yangzhou/China. 4. Cheng H.J., Wang L., (2021). The impact of animal welfare on breeding efficiency. Journal of Zhejiang Agricultural Sciences, Vol.62, Issue 10, pp.2084-2086+2090, Zhejiang/China. 5. Guo H., Ma X., Ma Q., (2017). LSSA_CAU: An interactive 3d point clouds analysis software for body measurement of livestock with similar forms of cows or pigs. Computers and Electronics in Agriculture,Vol.138, pp.60-68, Beijing/China.
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