College of Animal Sciences and Technology, Northeast Agriculture University, Harbin 150030, PR China.
College of Electrical Engineering and Information, Northeast Agricultural University, Harbin 150030, PR China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling 712100, PR China.
J Dairy Sci. 2019 Nov;102(11):10140-10151. doi: 10.3168/jds.2018-16164. Epub 2019 Sep 11.
Body condition score (BCS) is a common tool for indirectly estimating the mobilization of energy reserves in the fat and muscle of cattle that meets the requirements of animal welfare and precision livestock farming for the effective monitoring of individual animals. However, previous studies on automatic BCS systems have used manual scoring for data collection, and traditional image extraction methods have limited model performance accuracy. In addition, the radio frequency identification device system commonly used in ranching has the disadvantages of misreadings and damage to bovine bodies. Therefore, the aim of this research was to develop and validate an automatic system for identifying individuals and assessing BCS using a deep learning framework. This work developed a linear regression model of BCS using ultrasound backfat thickness to determine BCS for training sets and tested a system based on convolutional neural networks with 3 channels, including depth, gray, and phase congruency, to analyze the back images of 686 cows. After we performed an analysis of image model performance, online verification was used to evaluate the accuracy and precision of the system. The results showed that the selected linear regression model had a high coefficient of determination value (0.976), and the correlation coefficient between manual BCS and ultrasonic BCS was 0.94. Although the overall accuracy of the BCS estimations was high (0.45, 0.77, and 0.98 within 0, 0.25, and 0.5 unit, respectively), the validation for actual BCS ranging from 3.25 to 3.5 was weak (the F1 scores were only 0.6 and 0.57, respectively, within the 0.25-unit range). Overall, individual identification and BCS assessment performed well in the online measurement, with accuracies of 0.937 and 0.409, respectively. A system for individual identification and BCS assessment was developed, and a convolutional neural network using depth, gray, and phase congruency channels to interpret image features exhibited advantages for monitoring thin cows.
体况评分（BCS）是一种间接估计牛体脂肪和肌肉能量储备动员的常用工具，符合动物福利和精准养殖对个体动物进行有效监测的要求。然而，先前的自动 BCS 系统研究使用手动评分进行数据收集，传统的图像提取方法限制了模型性能的准确性。此外，牧场中常用的射频识别设备系统存在误读和牛体损伤的缺点。因此，本研究旨在开发和验证一种使用深度学习框架识别个体和评估 BCS 的自动系统。这项工作使用超声背脂厚度开发了 BCS 的线性回归模型，以确定训练集的 BCS，并测试了一个基于卷积神经网络的系统，该系统有 3 个通道，包括深度、灰度和相位一致性，用于分析 686 头奶牛的背部图像。在对图像模型性能进行分析后，使用在线验证来评估系统的准确性和精度。结果表明，所选的线性回归模型具有较高的决定系数值（0.976），手动 BCS 与超声 BCS 之间的相关系数为 0.94。虽然 BCS 估计的整体准确性较高（0、0.25 和 0.5 单位分别为 0.45、0.77 和 0.98），但实际 BCS 范围为 3.25 至 3.5 的验证结果较弱（0.25 单位范围内的 F1 分数分别仅为 0.6 和 0.57）。总体而言，在线测量的个体识别和 BCS 评估效果良好，准确率分别为 0.937 和 0.409。开发了一种个体识别和 BCS 评估系统，使用深度、灰度和相位一致性通道的卷积神经网络解释图像特征，在监测瘦牛方面具有优势。
