用深度学习从深度图像预测小牛体重，精度超90%。
Predicting Dairy Calf Body Weight from Depth Images Using Deep Learning (YOLOv8) and Threshold Segmentation with Cross-Validation and Longitudinal Analysis
- 用YOLOv8模型自动分割小牛身体区域，准确率达98%
- 单次测量预测中XGBoost误差仅4.37%，纵向分析用LMM误差更低
- 适合牧场自动化管理，尤其适合早期非接触式体重监测
断奶前监测小牛体重对评估生长、饲料效率、健康状况和断奶时机至关重要，但人工称重受限于人力、时间和设施。荷斯坦犊牛毛色复杂，且少有研究探索早期非接触式图像测量预测后期体重。本研究旨在：(1) 开发基于深度学习的分割模型提取小牛体征；(2) 比较深度学习与阈值分割方法；(3) 采用单次时间点交叉验证（线性回归、XGBoost）和多次时间点交叉验证（线性回归、XGBoost、线性混合模型）评估体重预测。采集63头荷斯坦和5头泽西犊牛的深度图像，其中20头荷斯坦犊牛进行手动称重。结果表明，YOLOv8分割模型（交并比=0.98）优于阈值法（0.89）。单次时间点预测中，XGBoost表现最佳（R²=0.91，MAPE=4.37%），而线性混合模型在纵向预测中精度最高（R²=0.99，MAPE=2.39%）。研究证明深度学习可用于自动化体重预测，提升牧场管理效率。
原文摘要 · Abstract (English)
Monitoring calf body weight (BW) before weaning is essential for assessing growth, feed efficiency, health, and weaning readiness. However, labor, time, and facility constraints limit BW collection. Additionally, Holstein calf coat patterns complicate image-based BW estimation, and few studies have explored non-contact measurements taken at early time points for predicting later BW. The objectives of this study were to (1) develop deep learning-based segmentation models for extracting calf body metrics, (2) compare deep learning segmentation with threshold-based methods, and (3) evaluate BW prediction using single-time-point cross-validation with linear regression (LR) and extreme gradient boosting (XGBoost) and multiple-time-point cross-validation with LR, XGBoost, and a linear mixed model (LMM). Depth images from Holstein (n = 63) and Jersey (n = 5) pre-weaning calves were collected, with 20 Holstein calves being weighed manually. Results showed that You Only Look Once version 8 (YOLOv8) deep learning segmentation (intersection over union = 0.98) outperformed threshold-based methods (0.89). In single-time-point cross-validation, XGBoost achieved the best BW prediction (R^2 = 0.91, mean absolute percentage error (MAPE) = 4.37%), while LMM provided the most accurate longitudinal BW prediction (R^2 = 0.99, MAPE = 2.39%). These findings highlight the potential of deep learning for automated BW prediction, enhancing farm management.
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