Manual weighing of heifers is time-consuming, labour-intensive, expensive, and can be dangerous and risky for both humans and animals because it requires the animal to be stationary. To overcome this problem, automated approaches have been developed using computer vision techniques. In this research, the aim was to design a novel mass prediction model using deep learning algorithms for youngstock on dairy farms. The Mask-RCNN segmentation algorithm was used to segment the images of heifers and remove the background. A convolutional neural networks (CNN) model was developed on the Keras platform to predict the body mass of heifers. For the case study, a new dataset based on images of 63 heifers was built. Animals were between the age of 0 and 365 days and lived on the same farm in the Netherlands. The range of body mass of the heifers was between 37 kg and 370 kg. The side-view model had a coefficient of determination (R²) of 0.91 and a Root Mean Squared Error (RMSE) of 27 kg, the top-view model had an R² of 0.96 and an RMSE of 20 kg. The experimental results demonstrated that our proposed mass prediction model using the Mask-RCNN segmentation algorithm, together with a novel CNN-based model, provided remarkable results, and that the top view was more suitable than the side view for predicting the body mass of youngstock in dairy farms.

手工称重小母牛是费时，费力，昂贵的，并且对于人和动物都可能是危险和危险的，因为这需要使动物静止。为了克服这个问题，已经使用计算机视觉技术开发了自动方法。在这项研究中，目标是使用深度学习算法为奶牛场的幼畜设计一个新颖的质量预测模型。Mask-RCNN分割算法用于分割小母牛的图像并去除背景。在Keras平台上开发了卷积神经网络（CNN）模型来预测小母牛的体重。对于案例研究，建立了一个基于63个小母牛图像的新数据集。动物的年龄介于0至365天之间，并居住在荷兰的同一农场。小母牛的体重范围在37公斤至370公斤之间。侧视图模型具有确定系数（R²）为0.91，均方根误差（RMSE）为27 kg，顶视图模型的R ²为0.96，RMSE为20 kg。实验结果表明，我们提出的使用Mask-RCNN分割算法的质量预测模型以及基于CNN的新型模型提供了显着的结果，并且顶视图比侧视图更适合于预测幼体的体重在奶牛场。