肉牛生产性能智能监测技术研究进展

doi:10.3864/j.issn.0578-1752.2025.23.021

Abstract

Abstract:

With the rapid development of large-scale beef breeding in our country, modern smart beef breeding technology, including the Internet of Things, big data and artificial intelligence, has been continuously improved. The identification of individual beef cattle and real-time monitoring of production performance, such as body weight, body size, and feed intake, are crucial for improving feeding management, reducing personnel workload, and accelerating the breeding process of beef cattle. Individual identification is the foundation for monitoring individual production performance. Current methods primarily rely on RFID identification technology and image-based deep learning individual identification technology. While RFID offers high accuracy, it faces challenges such as high cost, short identification distance, and significant workload for tagging. Image-based deep learning identification technology identifies individuals by analyzing unique biometric features like body surface patterns, ear tag text, nose prints, iris, retina, facial features, and side profiles. However, its effectiveness can be affected by lighting conditions and individual differences. In the future, it is necessary to develop precise, rapid and dynamic recognition machine vision recognition technology for beef cattle that can adapt to different environmental conditions. Images captured by 2D and 3D cameras can be used for estimation of body size and weight after key feature extraction and analysis. 2D cameras have the advantages of simple equipment acquisition and low cost. However, its reliance on reference objects of known dimensions during the measurement process, as well as the measurement limitations of curved surface characteristic body size indicators such as chest circumference and abdominal circumference, directly affect the accuracy of related body size measurement and body weight estimation. In contrast, 3D cameras can obtain the external structure of beef cattle and the distance information between them and the equipment in a comprehensive and three-dimensional manner, thus providing the possibility for precise measurement of multi-dimensional body weight indicators. Automated monitoring of beef cattle feed intake is vital for assessing feed efficiency. Automatic weighing feed troughs accurately measure intake by calculating the weight difference before and after feeding using pressure sensors. However, the challenges such as high installation costs and operational inconvenience have largely confined their application scope. Feed intake can also be effectively estimated through depth image changes before and after feeding or by recording feeding behavior by using relevant sensors. Nevertheless, in practical applications, the complexity of feed composition can affect monitoring accuracy. The technology for determining the production performance of beef cattle based on machine vision has made remarkable progress. However, it still faces many challenges, such as large amounts of data processing, environmental interference affecting the accuracy of results, and insufficient data development and utilization. In the future, strategies such as edge computing technology and optimizing phased detection can be adopted to reduce the computing pressure of device data and improve the agility of system response. Exploring 3D reconstruction technology based on single-view depth cameras could improve the feasibility of applying body dimension and weight monitoring in practical production settings. Efforts should be dedicated to developing universal prediction models applicable to different breeds and various growth stages to enhance the versatility and practicality of the technology. Strengthening multimodal data fusion will improve the comprehensive application of beef cattle production performance monitoring data. Intelligent monitoring technology is the key to the modernization of beef cattle breeding. Through technological innovation and integration, it is expected to achieve low-cost, high-precision and widely applicable intelligent monitoring technology for the production performance of beef cattle, promoting the intelligent upgrade of the beef cattle industry, improving production efficiency and economic benefits, and meeting market demands. This review summarized intelligent monitoring technologies for beef cattle identification, as well as for estimating body weight, body size, and feed intake. It also discussed the challenges and future development trends of intelligent monitoring technology for beef cattle production performance in China, aiming to provide references for the research and application of related intelligent monitoring technologies.

Key words: beef cattle, production performance, intelligent, body size, weight, deep learning, machine vision

ZHANG Fan, TANG XiangFang, YANG Liang, WANG Hui, CHEN RuiPeng, XIONG BenHai. Research Progress of Intelligent Monitoring Technology for Beef Cattle Production Performance[J].Scientia Agricultura Sinica, 2025, 58(23): 5081-5096.

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Figures/Tables 3

Table 1

Comparison of beef cattle individual identification technology"

监测部位 Monitoring site	监测设备 Monitoring equipment	主要识别算法 Main identification method	准确率 Accuracy rate	特点 Characteristic	参考文献 Reference
耳标文本 Ear tag text	三千万像素照相机 30-megapixel camera	CRNN	92.30%	利用耳标进行文本识别，但对图像采集角度及摄像机的像素要求较高 Text recognition using ear tags is possible but needs specific image acquisition angles and high camera resolution	[22]
耳标文本 Ear tag text	SP007 Sricam IP防水摄像机与红外夜视仪 SP007 Sricam IP waterproof camera with infrared night vision device	YOLOv3	> 95%	引入纠错算法，避免误读 An ad hoc error correction algorithm is presented to avoid misreading	[12]
鼻纹 Muzzle	X-T4相机 X-T4 camera	VGG	98.7%	通过加权交叉熵损失函数和数据增强处理提高结果精度 The identification accuracy is improved through the weighted cross-entropy loss function and data augmentation processing	[23]
虹膜 Iris	二维图像 2D image	2DLP-LDA	94.07%	优秀的泛化能力，可识别存在局部遮挡和形变等质量缺陷的图像 Good generalization ability and can identify images with quality defects such as local occlusion and deformation	[26]
视网膜 Retina	二维图像 2D image	SURF	92.25%	利用视网膜血管识别，准确率高 The recognition by retinal blood vessels has a high accuracy rate	[27]
面部 Face	索尼A6000和华为P30 SONY A6000 and Huawei P30	MobileNetV1为主干网络，K-means++聚类 MobileNetV1 as the backbone network, and K-means++ clustering algorithm	无遮挡时99.86%；遮挡小于30%时90%以上 99.86% with no occlusion, and over 90% when occlusion is less than 30%	在遮挡情况下仍可识别 It can still be recognized even in occluded conditions	[30]
面部 Face	二维图像 2D image	YOLO-Unet组合网络模型 YOLO-Unet combined network	90.48%	引入背景消去模块，提高准确率11.99% The model with background removal improve recognition accuracy of 11.99%	[28]
面部 Face	二维图像 2D image	SOLOv2	98.06%	添加SOLOv2实例分割模型获取牛脸轮廓信息，提高牛面部识别准确率 Add the SOLOv2 instance segmentation model to obtain the contour information of cow faces and improve the accuracy of cow face recognition	[29]
侧面 Body side	华为Mate30摄像机 Huawei Mate30 camera	TLAINS-InceptionV3	99.74%	解决肉牛无明显自身特征带来的识别准确率问题 Solve the problem of recognition accuracy caused by the lack of obvious self-characteristics of beef cattle	[32]

Table 1

Table 2

Research and comparison of measuring techniques for beef cattle body size"

监测部位 Monitoring site	测定指标 Monitoring parameters	监测设备 Monitoring equipment	主要识别算法 Main identification method	准确率 Accuracy rate	特点 Characteristic	参考文献 Reference
侧面 Body side	体高、体斜长、胸深、蹄径 Body height, body oblique length, chest depth, hoof diameter	单个RGB相机 Single RGB camera	YOLO v5s对肉牛目标进行定位，Lite-HRNet提取关键点 YOLO v5s detects the beef cattle target, and Lite-HRNet detects the key points	相对误差分别6.75%、7.55%、8.00%、8.97% With average relative error of 6.75 %, 7.55 %, 8.00 % and 8.97%	能准确测量不同距离和光照条件下的体尺，但结果易受背景和拍摄角度影响 Accurately measure the body sizes under different distances and lighting conditions, but the accuracy is affected by the background and shooting angle	[39]
侧面 Body side	体高、体长 Body height, body length	二维图像 2D image	Image 2	误差小于0.04 m Average prediction deviation < 0.04 m	成本低，但胸围预测准确率低 Low cost, but low prediction accuracy of chest circumference	[40]
侧面 Body side	体高、体斜长 Body height, body oblique length	二维图像 2D image	YOLOv5	相对误差分别为4.49%、4.70% With relative error of 4.49% and 4.70%	以已知尺寸的耳标为参照计算体尺 Using the ear tag of known size as reference to calculate body sizes	[41]
侧面 Body side	体高，胸深、背高、体斜长、腰高 Body height, chest depth, back height, body oblique length, waist height	单个IFM O3D303 3D激光雷达 IFM O3D303 3D LiDAR camera	GPT三维重建 Greedy Projection Triangulation reconstruction	精度2 mm，相对误差< 2% With accuracy of 2 mm and relative error close to 2%	精度高，但操作复杂，且动物需要保持静止 High precision, but the operation is complex and the animal needs to remain stationary	[42]
5个角度 Five angles	体高、臀高、胸围、腹围、体斜长 Body height, hip height, chest circumference, abdominal circumference, body oblique length	5个Kinect DK Five Kinect DK sensors	BTSS进行关键区域分割，基于姿态的调整模型进行结果校正 Segmentation of key regions by bidirectional tomographic slice segmentation method, along with Posture-based Measurement Adjustment model to correct result	相对误差分别为1.84%、3.47%、1.56%、2.36%、1.14% With relative errors 1.84%, 3.47%, 1.56% 2.36% and 1.14%	多摄像头实时三维重建，精确度高 Multi-camera real-time 3D reconstruction with high accuracy	[43]
侧面 Body side	体高、胸围、背高、腰高、体斜长 Body height, chest circumference, back height, waist height, body oblique length	单个IFM O3D303 Single IFM O3D303	按照动物左右对称原理合并后，经孔洞修复后计算体尺 After merging according to the principle of left-right symmetry of animals, the body sizes were calculated after hole repair	平均相对误差5.42% With average relative error 5.42%	基于对称原理构建真实肉牛轮廓用于体尺测量 The real beef cattle contour was constructed based on the principle of symmetry for body size measurement	[44]

Table 2

Table 3

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监测部位 Monitoring site	监测设备 Monitoring equipment	主要识别算法 Main identification method	准确率 Accuracy rate	特点 Characteristic	参考文献 Reference
背部 Back	MD-1004NS MD-DVR41	Bagging	相对误差2.67%，误差值13.44 kg With relative error 2.67%, and average error value 13.44 kg	采用背部二维图像估测体重 Estimate the body weight by two-dimensional images of the back	[55]
侧面 Body side	IFM O3D303	PointNet++确定体尺测定关键点 PointNet++ determines the key points for measuring the body sizes	相对误差3.2%，误差值10.2 kg With relative error 3.2%, and average error value 10.2 kg	依据深度相机测定的体尺估算体重 Estimate the body weight based on the body size measured by the depth camera	[45]
背部 Back	Intel RealSense D435i	MLP	相对误差3.13% With relative error 3.13%	利用体积估算体重 Estimate body weight by volume	[56]
侧面 Body side	LiDAR	Potree Desktop	相对误差6.3% With relative error 6.3%	在无人机上搭载深度相机估测体重 Estimate body weight by a depth camera installed on the unmanned aerial vehicle	[57]
背部 Back	Kinect® model 1473	ANN	体重相对误差< 5.32%，日增重误差均< 0.10 kg·d^-1 With relative error of body weight < 5.32%, and the error value of average daily gain all < 0.10 kg·d^-1	同一模型预测肉牛不同阶段体重，并计算日增重 Estimate the body weight and average daily gain of beef cattle at different stages by the same model	[58]

Research Progress of Intelligent Monitoring Technology for Beef Cattle Production Performance

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