Department of Neurology, University of Wisconsin-Madison, Madison, WI 53703, USA.
Department of Animal and Dairy Sciences, University of Wisconsin-Madison, -Madison, WI 53703, USA.
J Anim Sci. 2022 Sep 1;100(9). doi: 10.1093/jas/skac242.
The use of sexed semen at dairy farms has improved heifer replacement over the last decade by allowing greater control over the number of retained females and enabling the selection of dams with superior genetics. Alternatively, beef semen can be used in genetically inferior dairy cows to produce crossbred (beef x dairy) animals that can be sold at a higher price. Although crossbreeding became profitable for dairy farmers, meat cuts from beef x dairy crosses often lack quality and shape uniformity. Technologies for quickly predicting carcass traits for animal grouping before harvest may improve meat cut uniformity in crossbred cattle. Our objective was to develop a deep learning approach for predicting ribeye area and circularity of live animals through 3D body surface images using two neural networks: 1) nested Pyramid Scene Parsing Network (nPSPNet) for extracting features and 2) Convolutional Neural Network (CNN) for estimating ribeye area and circularity from these features. A group of 56 calves were imaged using an Intel RealSense D435 camera. A total of 327 depth images were captured from 30 calves and labeled with masks outlining the calf body to train the nPSPNet for feature extraction. Additional 42,536 depth images were taken from the remaining 26 calves along with three ultrasound images collected for each calf from the 12/13th ribs. The ultrasound images (three by calf) were manually segmented to calculate the average ribeye area and circularity and then paired with the depth images for CNN training. We implemented a nested cross-validation approach, in which all images for one calf were removed (leave-one-out, LOO), and the remaining calves were further divided into training (70%) and validation (30%) sets within each LOO iteration. The proposed model predicted ribeye area with an average coefficient of determination (R2) of 0.74% and 7.3% mean absolute error of prediction (MAEP) and the ribeye circularity with an average R2 of 0.87% and 2.4% MAEP. Our results indicate that computer vision systems could be used to predict ribeye area and circularity in live animals, allowing optimal management decisions toward smart animal grouping in beef x dairy crosses and purebred.
在过去的十年中，奶牛场使用性别鉴定精液通过更好地控制保留雌性动物的数量和选择具有优良遗传特性的母畜，提高了后备牛的更换率。或者，可以在遗传性能较低的奶牛中使用牛肉精液，生产可以高价出售的杂交（牛肉 x 奶牛）动物。尽管杂交对奶牛场有利可图，但来自牛肉 x 奶牛杂交的肉切块往往缺乏质量和形状的一致性。在收获前对动物进行分组的快速预测胴体特征的技术可以提高杂交牛的肉切块均匀度。我们的目标是开发一种深度学习方法，通过使用两个神经网络从 3D 体表面图像中预测活牛的眼肌面积和圆形度：1）嵌套金字塔场景解析网络（nPSPNet）用于提取特征，2）卷积神经网络（CNN）用于从这些特征估计眼肌面积和圆形度。一组 56 头小牛使用英特尔 RealSense D435 相机进行成像。从 30 头小牛中总共捕获了 327 张深度图像，并使用标记小牛身体的遮罩对其进行了标记，以训练 nPSPNet 进行特征提取。另外，从其余 26 头小牛中还采集了 42536 张深度图像，并为每头小牛从第 12/13 肋骨采集了三张超声图像。手动分割超声图像（每头小牛三张）以计算平均眼肌面积和圆形度，然后将其与深度图像配对以进行 CNN 训练。我们实现了一种嵌套交叉验证方法，其中一头小牛的所有图像（留一法，LOO）都被删除，其余的小牛在每个 LOO 迭代中进一步分为训练（70%）和验证（30%）集。所提出的模型预测眼肌面积的平均决定系数（R2）为 0.74%，平均预测误差（MAEP）为 7.3%，预测眼肌圆形度的平均 R2 为 0.87%，平均 MAEP 为 2.4%。我们的结果表明，计算机视觉系统可用于预测活牛的眼肌面积和圆形度，从而可以对牛肉 x 奶牛杂交和纯种牛进行智能动物分组的最佳管理决策。
