McPhee M J, Walmsley B J, Skinner B, Littler B, Siddell J P, Cafe L M, Wilkins J F, Oddy V H, Alempijevic A
J Anim Sci. 2017 Apr;95(4):1847-1857. doi: 10.2527/jas.2016.1292.
The objective of this study was to develop a proof of concept for using off-the-shelf Red Green Blue-Depth (RGB-D) Microsoft Kinect cameras to objectively assess P8 rump fat (P8 fat; mm) and muscle score (MS) traits in Angus cows and steers. Data from low and high muscled cattle (156 cows and 79 steers) were collected at multiple locations and time points. The following steps were required for the 3-dimensional (3D) image data and subsequent machine learning techniques to learn the traits: 1) reduce the high dimensionality of the point cloud data by extracting features from the input signals to produce a compact and representative feature vector, 2) perform global optimization of the signatures using machine learning algorithms and a parallel genetic algorithm, and 3) train a sensor model using regression-supervised learning techniques on the ultrasound P8 fat and the classified learning techniques for the assessed MS for each animal in the data set. The correlation of estimating hip height (cm) between visually measured and assessed 3D data from RGB-D cameras on cows and steers was 0.75 and 0.90, respectively. The supervised machine learning and global optimization approach correctly classified MS (mean [SD]) 80 (4.7) and 83% [6.6%] for cows and steers, respectively. Kappa tests of MS were 0.74 and 0.79 in cows and steers, respectively, indicating substantial agreement between visual assessment and the learning approaches of RGB-D camera images. A stratified 10-fold cross-validation for P8 fat did not find any differences in the mean bias ( = 0.62 and = 0.42 for cows and steers, respectively). The root mean square error of P8 fat was 1.54 and 1.00 mm for cows and steers, respectively. Additional data is required to strengthen the capacity of machine learning to estimate measured P8 fat and assessed MS. Data sets for and continental cattle are also required to broaden the use of 3D cameras to assess cattle. The results demonstrate the importance of capturing curvature as a form of representing body shape. A data-driven model from shape to trait has established a proof of concept using optimized machine learning techniques to assess P8 fat and MS in Angus cows and steers.
本研究的目的是为使用现成的红绿蓝深度（RGB-D）微软Kinect相机客观评估安格斯母牛和公牛的第8肋臀部脂肪（P8脂肪；毫米）和肌肉评分（MS）性状建立一个概念验证。在多个地点和时间点收集了来自低肌肉量和高肌肉量牛（156头母牛和79头公牛）的数据。对于三维（3D）图像数据及随后用于学习这些性状的机器学习技术，需要以下步骤：1）通过从输入信号中提取特征来降低点云数据的高维性，以生成一个紧凑且具有代表性的特征向量；2）使用机器学习算法和并行遗传算法对特征进行全局优化；3）使用回归监督学习技术针对数据集中每头动物的超声P8脂肪进行训练，并使用分类学习技术对评估的MS进行训练。在母牛和公牛中，RGB-D相机视觉测量的和评估的3D数据之间估计髋部高度（厘米）的相关性分别为0.75和0.90。监督式机器学习和全局优化方法对母牛和公牛MS的正确分类率分别为80（4.7）%和83%[6.6%]。母牛和公牛MS的Kappa检验分别为0.74和0.79，表明视觉评估与RGB-D相机图像学习方法之间有实质性一致性。对P8脂肪进行的分层10折交叉验证未发现母牛和公牛的平均偏差有任何差异（母牛和公牛分别为 = 0.62和 = 0.42）。母牛和公牛P8脂肪的均方根误差分别为1.54和1.00毫米。需要更多数据来增强机器学习估计测量的P8脂肪和评估的MS的能力。还需要针对和大陆牛的数据集，以扩大3D相机在评估牛方面的应用。结果证明了捕捉曲率作为表示身体形状的一种形式的重要性。一个从形状到性状的数据驱动模型已使用优化的机器学习技术建立了一个概念验证，用于评估安格斯母牛和公牛的P8脂肪和MS。
