畜禽养殖疾病诊断智能传感技术研究进展
李奇峰,李嘉位,马为红,高荣华,余礼根,丁露雨,于沁杨

Research Progress of Intelligent Sensing Technology for Diagnosis of Livestock and Poultry Diseases
LI QiFeng,LI JiaWei,MA WeiHong,GAO RongHua,YU LiGen,DING LuYu,YU QinYang
表4 疾病自动诊疗主流代表方法
Table 4 Mainstream representative methods of automatic disease diagnosis
诊断依据
Diagnostic basis
技术手段
Technical means
主要用途
The main purpose
识别精度
identification accuracy
代表文献
The literature
形态特征
Morphological characteristics
SVM分类 SVM classification

深度学习分类 Deep learning classify
疾病诊病/敏感特征提取
Disease diagnosis/ feature extraction
97.5%
-97.8%
91.7%
[7]

[20]
动作姿态
Action & posture
三轴加速传感器 Acceleration sensor
SVM分类 SVM classification
视频特征识别 Video feature recognition
深度学习分类 Deep learning classify
动物姿态与健康状态评价
Posture and health evaluation
80%
94%
90%
99%
[60]
[9]
[22][46]
[47]
位置特征
Location feature
视频光流法 Video optical flow
UWB定位模块法 Positioning module
深度学习目标检测 Deep learning Target Detection
运动量检测、采食量、饮水量估计等
Exercise detection, estimated feed intake and water consumption
较低 Lower
最高 Highest
较高 Higher
[52]
[101]
[49][50]
声音特征
Voice characteristics
发声图谱分析 Voice atlas analysis
偏度聚类分析 Skewness cluster analysis
疾病诊断及应激评价
Disease diagnosis & stress evaluation
88%
95%
[71][75]
[70]
呼吸特征
Respiratory characteristics
稀疏光流法 Sparse optical flow
WIFI感知法 WIFI perception method
深度图像分析 Depth image analysis
穿戴式传感器 Wearable sensor
代谢评价及舒适度评价
Metabolic & Comfort evaluation
98.58%
约98%
85.3%
[63]
[92]
[48]
[96]
体温特征
Body temperature
植入传感器 Implanted sensor
红外测温法 Infrared Thermometry
疾病诊断及应激评价
Disease diagnosis & stress evaluation
±0.05℃
±1.5℃
[80][87]
[86][88]