Evaluating transfer learning strategies for improving dairy cattle body weight prediction in small farms using depth-image and point-cloud data

2026-01-03

arXiv.org

AI Summary

This study addresses the challenge of accurately predicting dairy cow body weight in small-scale farms, where limited data availability and barriers to cross-farm data sharing hinder model performance. For the first time, it systematically compares deep learning approaches using depth images (via ConvNeXt and MobileViT) and point clouds (via PointNet and DGCNN) within a transfer learning framework. The work demonstrates that effective cross-farm knowledge transfer can be achieved solely by sharing pre-trained model weights—without exchanging raw data. Experimental results show that transfer learning substantially improves prediction accuracy on small farms, outperforming single-source training and achieving performance comparable to joint multi-farm training. Moreover, no statistically significant difference is observed between the two modalities in terms of predictive efficacy.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• transfer learning
• body weight prediction
• dairy cattle
• depth image
• point cloud

Innovation

• transfer learning
• depth image
• point cloud
• body weight prediction
• dairy cattle