Advances and Challenges in Physiological Parameters Monitoring and Diseases Diagnosing of Dairy Cows Based on Computer Vision

doi:10.12133/j.smartag.SA202204005

Abstract

Abstract:

Realizing the construction of intelligent farming by using advanced information technology, thus improving the living welfare of dairy cows and the economic benefits of dairy farms has become an important goal and task in dairy farming research field. Computer vision technology has the advantages of non-contact, stress-free, low cost and high throughput, and has a broad application prospect in animal production. On the basis of describing the importance of computer vision technology in the development of intelligent farming industry, this paper introduced the cutting-edge technology of cow physiological parameters and disease diagnosis based on computer vision, including cow temperature monitoring, body size monitoring, weight measurement, mastitis detection and lameness detection. The introduction coverd the development process of these studies, the current mainstream techniques, and discussed the problems and challenges in the research and application of related technology, aiming at the problem that the current computer vision-based detection methods are susceptible to individual difference and environmental changes. Combined with the development status of farming industry, suggestions on how to improve the universality of computer vision technology in intelligent farming industry, how to improve the accuracy of monitoring cows' physiological parameters and disease diagnosis, and how to reduce the influence of environment on the system were put forward. Future research work should focus on research and developmentof algorithm, make full use of computer vision technology continuous detection and the advantage of large amount of data, to ensure the accuracy of the detection, and improve the function of the system integration and data utilization, expand the computer vision system function. Under the premise that does not affect the ability of the system, to improve the study on the number of function integration and system function and reduce equipment costs.

Key words: dairy farming, computer vision technology, physiological parameters monitoring, diseases diagnosing, precision livestock farming, intelligent farming

CLC Number:

KANG Xi, LIU Gang, CHU Mengyuan, LI Qian, WANG Yanchao. Advances and Challenges in Physiological Parameters Monitoring and Diseases Diagnosing of Dairy Cows Based on Computer Vision[J]. Smart Agriculture, 2022, 4(2): 1-18.

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文献	年份	设备类型	技术	体尺	研究结果	样本量/个
Stajnko等^［16］	2008	热成像仪	数字图像处理	体高、臀高	标准差为0.10~0.433 cm	12
Taşdemir等^［17］	2011	可见光相机	数字图像处理	肩高、臀高、臀宽、体长	平均精度为97.215%	115
郭浩等^［18］	2014	深度相机	三维点云处理	胸宽、尻宽、前乳头长度等	相对误差小于10%	——
Marinello等^［19］	2015	深度相机	三维点云处理	臀宽、体长、体高、胸围等	手工与算法测量的平均决定系数为0.929	20
Nir等^［20］	2018	深度相机	数字图像处理	体高、臀高	手工与算法测量的平均决定系数为0.969	107
Pezzuolo等^［21］	2018	深度相机	三维点云处理	臀宽、体长、体高、胸深等	相对误差小于6%	20
Le Cozler等^［22］	2019	3D扫描仪	三维点云处理	肩高、臀宽、体宽、坐骨宽度等	手工与3D测量之间平均相关性为0.75	30
李琦等^［23］	2020	可见光相机	三维点云处理	体长、体高、体斜长	平均相对误差为6.24%	15
初梦苑^［24］	2020	深度相机	三维点云处理	胸围	平均相对误差为3.30%	86
Kamchen等^［25］	2021	RGB-D相机	深度学习、数字图像处理	臀宽、臀长、臀高、肩高	平均相对误差为11.58%	260

文献	年份	相机类型	技术	研究目标	研究结果	样本量/个
Hoffmann等^［36］	2013	OPTRIS® PI 160热红外相机	Kenward-Roger近似检验	奶牛身体各部位和外阴温度差异	奶牛眼睛与外阴温度差异最小	22
Salles等^［37］	2016	FLIR Fluke Ti20TM热像仪	Pearson相关性分析	奶牛身体各部位和直肠温度相关性	奶牛前额与直肠温度相关性达到0.9	24
Peng等^［38］	2019	VarioCAM®热红外相机	单因素方差分析	不同温湿度环境下奶牛体表温度和直肠温度变化	体表温度比直肠温度对环境变化更敏感	488
何金成等^［39］	2020	FLIR E60便携式红外热像仪	相关性分析	环境温湿度对体表温度检测影响	环境温度影响较大；眼温与直肠温相关性最高	200
何东健等^［32］	2021	MAG62型热像仪	骨架树模型	奶牛眼温检测	平均绝对误差0.35 ℃、平均相对误差为0.38%	40
Jaddoa等^［40］	2021	FLIR A310、 T420 热红外相机	机器学习与阈值处理	奶牛眼温检测	平均精度为0.72，敏感性0.98	35

文献	年份	设备类型	技术	模型参数	准确率	样本量/个
Tasdemir等^［46］	2011	可见光相机	多元线性回归	体尺	平均相对误差为1.87%	16
张立倩^［47］	2013	可见光相机	模糊逼近算法	体尺	平均相对误差为2.00%	——
Song等^［48］	2017	Kinect v2	多元线性回归	体尺、年龄、胎次	平均相对误差为5.20%	30
牛金玉^［49］	2018	Kinect v2	最小二乘法	体尺	相对均方根误差为2.87%	45
Le Cozler等^［50］	2019	Morpho2D	多元线性回归	体尺、体积、表面积	平均相对误差为2.72%	64
初梦苑等^［51］	2020	Kinect v2	多元线性回归	体积、表面积	平均相对误差为2.04%	86

文献	年份	相机类型	技术	乳房炎检测指标	研究结果	样本量/个
郭艳娇等^［76］	2022	Fotric-625c红外热像仪	线刨法，温度拟合线	奶牛乳房温度拟合线的斜率	奶牛左右患病乳房识别准确率为75%	189
王彦超等^［77］	2021	FLIR-A615	深度学习	眼睛乳房温差法	奶牛乳房炎分类准确率为77.3%	22
张旭东等^［41］	2019	FLIR-A615	热红外图像处理	眼睛乳房温差法	对于乳房炎分类准确率为87.5%	17
Zhang等^［42］	2020	FLIR-A615	深度学习	眼睛乳房温差法	奶牛乳房炎分类准确率为83.33%	30
蔡一欣^［65］	2017	5000USB 摄像头	pH 测试纸图像处理	——	平均相对误差为 3.67%，标准差为1.88%	25

文献	年份	相机类型	分类算法	跛行特征	研究结果	样本量/个
Poursaberi等^［92］	2010	可见光	阈值判别	弓背	灵敏度为100%，特异性为97.60%，准确率为94.70%	184
宋怀波等^［93］	2018	可见光	KNN ^①	头颈部轮廓	准确率为93.00%	30
Kang等^［94］	2020	可见光	阈值判别	对称性	准确率为96.00%	100
Jiang等^［95］	2022	可见光	BiLSTM ^②	弓背	准确率为96.61%	90
Kang等^［96］	2022	可见光	DenseNet	对称性、跟随性	灵敏度为98.50%，特异性为99.25%，准确率为98.50%	456
Viazzi等^［97］	2014	深度	决策树	弓背	灵敏度为82.00%，特异性为91.00%，准确率为90.00%	273
Jabbar等^［98］	2017	深度	SVM ^③	对称性	灵敏度为100.00%，特异性为75.00%，准确率为95.70%	22
Alsaaod和Büscher^［99］	2012	热红外	阈值判别	牛蹄温度	灵敏度为80.00%，特异性为82.90%	24
Alsaaod等^［100］	2014	热红外	阈值判别	前后牛蹄温度差	灵敏度为89.10%，特异性为66.60%	149
康熙等^［101］	2021	热红外	阈值判别	弓背	准确率为90.00%	160