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滕光辉,申志杰,张建龙,石晨,余炅桦.基于Kinect传感器的无接触式母猪体况评分方法[J].农业工程学报,2018,34(13):211-217.DOI:10.11975/j.issn.1002-6819.2018.13.025
基于Kinect传感器的无接触式母猪体况评分方法
投稿时间:2018-03-09  修订日期:2018-04-08
中文关键词:  图像处理  算法  模型  体况评分  Kinect  臀部高宽比  臀股面积  臀部曲率
基金项目:十三五国家重点研发计划(2016YFD0700204)
作者单位
滕光辉 中国农业大学水利与土木工程学院农业部设施农业工程重点实验室北京 100083 
申志杰 中国农业大学水利与土木工程学院农业部设施农业工程重点实验室北京 100083 
张建龙 中国农业大学水利与土木工程学院农业部设施农业工程重点实验室北京 100083 
石晨 中国农业大学水利与土木工程学院农业部设施农业工程重点实验室北京 100083 
余炅桦 中国农业大学水利与土木工程学院农业部设施农业工程重点实验室北京 100083 
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中文摘要:为了提高母猪的繁殖性能,减少传统方法给动物和估测人员带来的不利影响,该研究提出了一种可应用于实际生产中的准确、无接触式的母猪体况评分(body condition scoring, BCS)方法。试验使用Kinect传感器获取108组母猪臀股的三维图像,选取48组图像进行分析处理,计算出臀部的高宽比、臀股面积及曲率半径。试验结果表明:母猪臀部的高宽比、臀股面积和曲率半径与背膘厚度的相关系数分别为0.567、0.502、0.951;以曲率半径作为主要参数建立母猪体况估测模型。取剩余60组图像进行验证,估测模型计算结果与经验方法评估结果差异较小,准确率达到91.7 %;结果表明,基于三维重构技术的Kinect传感器能够实现母猪在饲养管理过程中对体况的无接触式检测。
Teng Guanghui,Shen Zhijie,Zhang Jianlong,Shi Chen,Yu Jionghua.Non-contact sow body condition scoring method based on Kinect sensor[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2018,34(13):211-217.DOI:10.11975/j.issn.1002-6819.2018.13.025
Non-contact sow body condition scoring method based on Kinect sensor
Author NameAffiliation
Teng Guanghui College of Water Conservancy and Civil Engineering, China Agricultural University, Ministry of Agriculture Facility Key Laboratory of Agricultural Engineering, Beijing 100083, China 
Shen Zhijie College of Water Conservancy and Civil Engineering, China Agricultural University, Ministry of Agriculture Facility Key Laboratory of Agricultural Engineering, Beijing 100083, China 
Zhang Jianlong College of Water Conservancy and Civil Engineering, China Agricultural University, Ministry of Agriculture Facility Key Laboratory of Agricultural Engineering, Beijing 100083, China 
Shi Chen College of Water Conservancy and Civil Engineering, China Agricultural University, Ministry of Agriculture Facility Key Laboratory of Agricultural Engineering, Beijing 100083, China 
Yu Jionghua College of Water Conservancy and Civil Engineering, China Agricultural University, Ministry of Agriculture Facility Key Laboratory of Agricultural Engineering, Beijing 100083, China 
Key words:image processing  algorithms  models  body condition score  Kinect  height-width ratio  hip area  hip curvature
Abstract:The body condition scoring (BCS) is an important tool of assessment method on body condition for sow raising and management. It has been divided into 5 grades that from emaciated to overly fat grade and each grade had a score. The traditional method has negative effect on animals and farmers, and the process is complex with excessive contact. The body size and shape of sows are correlated with their reproductive performance but are difficult to measure manually. There is subjective uncertainty in the process of manual measurement. The Kinect's 3D reconstruction technology was used to estimate and analyze the buttock shape of sows. A total of 108 images of Large White sows were manually acquired by Kinect camera during the feeding process at Feng Ning Animal Experimental Building in Chengde, Hebei Province of China, from July 25, 2017 to August 15, 2017. The hip height and hip width were measured by using tape and the back fat thickness was measured by using back fat measuring instrument. The hip height, hip width and area of buttock were obtained by analyzing the key points of 48 images. In order to obtain the measurement points of the livestock, several processing steps were taken, and the steps were as follow: 1) The sow stall was removed manually by Geomagic, and the target pig was acquired. In order to improve post-processing speed, the vertex culling algorithm was used to simplify the Three-dimensional model. 2) Since the models acquired were from different angles, the principal component analysis (PCA) was used to acquire new coordinate system. By using the geometrical relationship among the coordinate axes, standard measuring coordinate system was defined in this paper. 3) According to the geometric feature of the measurement points, the hip height, hip width and area of buttock were obtained. The results showed that the root mean square error of estimated body size was less than 2.1 cm, which meet the requirements of precision. The slice method was used to draw a curve at the highest point of the area of buttock based on the point cloud data. The least square fitting method was used to get the curve of hip contour. The hip radius of curvature was calculated by derivation formula. The results showed that the height-width ratio, area and radius of curvature of the sow's hip had the correlation with the back fat thickness. The correlation coefficients were 0.567, 0.502 and 0.951 respectively. With radius of curvature as the main parameter, the sow body condition estimation model was built based on the experience of hip morphology. 60 images were selected for validation. By comparing the measured and the estimated values of back fat thickness, the maximum absolute error of back fat detection is 1.3 mm and the average relative error is 3.7%. The accuracy of body condition assessment was 91.7% compared with the traditional methods. All the results mentioned above indicate that this study provides a non-contact body condition assessment method based on 3D reconstruction technology and has certain application potential in the real livestock productive.
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