Abstract
Computer vision provides automated, non-invasive, and scalable tools for monitoring dairy cattle, thereby supporting management, health assessment, and phenotypic data collection. Although transfer learning is commonly used for predicting body weight from images, its effectiveness and optimal fine-tuning strategies remain poorly understood in livestock applications, particularly beyond the use of pretrained ImageNet or COCO weights. In addition, while both depth images and three-dimensional point-cloud data have been explored for body weight prediction, direct comparisons of these two modalities in dairy cattle are limited. Therefore, the objectives of this study were to 1) evaluate whether transfer learning from a large farm enhances body weight prediction on a small farm with limited data, and 2) compare the predictive performance of depth-image- and point-cloud-based approaches under three experimental designs. Top-view depth images and point-cloud data were collected from 1,201, 215, and 58 cows at large, medium, and small dairy farms, respectively. Four deep learning models were evaluated: ConvNeXt and MobileViT for depth images, and PointNet and DGCNN for point clouds. Transfer learning markedly improved body weight prediction on the small farm across all four models, outperforming single-source learning and achieving gains comparable to or greater than joint learning. These results indicate that pretrained representations generalize well across farms with differing imaging conditions and dairy cattle populations. No consistent performance difference was observed between depth-image- and point-cloud-based models. Overall, these findings suggest that transfer learning is well suited for small farm prediction scenarios where cross-farm data sharing is limited by privacy, logistical, or policy constraints, as it requires access only to pretrained model weights rather than raw data.
Abstract (translated)
计算机视觉提供了自动化、无创性和可扩展的工具，用于监测乳牛，从而支持管理、健康评估和表型数据收集。尽管在从图像预测体重时通常使用迁移学习，但其在畜牧业应用中的有效性和最佳微调策略仍然在很大程度上不为人知，尤其是在预训练ImageNet或COCO权重之外的应用中。此外，虽然深度图像和三维点云数据已被探索用于体重预测，但在乳牛上的这两种模式之间的直接比较是有限的。因此，本研究的目标是：1）评估来自大型农场的迁移学习是否可以增强在小型农场上进行体重预测的能力，特别是在数据较少的情况下；2）在三种实验设计下，对比基于深度图像和点云的方法的预测性能。 研究人员从大型、中型和小型乳牛场分别收集了1,201头、215头和58头奶牛的俯视图深度图像和点云数据。评估了四种深度学习模型：用于深度图像的ConvNeXt和MobileViT，以及用于点云的PointNet和DGCNN。迁移学习显著提升了小型农场上所有四个模型的体重预测能力，优于单源学习，并且在联合学习中的增益相当或更大。这些结果表明，预训练表示能够很好地跨不同成像条件和乳牛群体的农场进行泛化。未观察到深度图像和点云方法之间的性能差异。 总体而言，这些发现表明迁移学习非常适合于小型农场预测场景，在这种情况下，由于隐私、物流或政策限制而无法共享跨农场数据的情况中，因为其只需要访问预训练模型权重而不是原始数据。
URL
https://arxiv.org/abs/2601.01044
