Consorzio Ricerca Filiera Lattiero-Casearia (CoRFiLaC), Regione Siciliana, 97100 Ragusa, Italy.
J Dairy Sci. 2011 Apr;94(4):2126-37. doi: 10.3168/jds.2010-3467.
Body condition score (BCS) is considered an important tool for management of dairy cattle. The feasibility of estimating the BCS from digital images has been demonstrated in recent work. Regression machines have been successfully employed for automatic BCS estimation, taking into account information of the overall shape or information extracted on anatomical points of the shape. Despite the progress in this research area, such studies have not addressed the problem of modeling the shape of cows to build a robust descriptor for automatic BCS estimation. Moreover, a benchmark data set of images meant as a point of reference for quantitative evaluation and comparison of different automatic estimation methods for BCS is lacking. The main objective of this study was to develop a technique that was able to describe the body shape of cows in a reconstructive way. Images, used to build a benchmark data set for developing an automatic system for BCS, were taken using a camera placed above an exit gate from the milking robot. The camera was positioned at 3 m from the ground and in such a position to capture images of the rear, dorsal pelvic, and loin area of cows. The BCS of each cow was estimated on site by 2 technicians and associated to the cow images. The benchmark data set contained 286 images with associated BCS, anatomical points, and shapes. It was used for quantitative evaluation. A set of example cow body shapes was created. Linear and polynomial kernel principal component analysis was used to reconstruct shapes of cows using a linear combination of basic shapes constructed from the example database. In this manner, a cow's body shape was described by considering her variability from the average shape. The method produced a compact description of the shape to be used for automatic estimation of BCS. Model validation showed that the polynomial model proposed in this study performs better (error=0.31) than other state-of-the-art methods in estimating BCS even at the extreme values of BCS scale.
体况评分（BCS）被认为是管理奶牛的重要工具。最近的研究已经证明，从数字图像估计 BCS 的可行性。回归机已成功用于自动 BCS 估计，考虑到整体形状的信息或从形状的解剖点提取的信息。尽管在这个研究领域取得了进展，但这些研究尚未解决建模奶牛形状的问题，以构建用于自动 BCS 估计的健壮描述符。此外，缺乏用于定量评估和比较不同自动 BCS 估计方法的基准图像数据集。本研究的主要目的是开发一种能够以重建方式描述奶牛体型的技术。用于构建用于开发自动 BCS 系统的基准数据集的图像是使用放置在挤奶机器人出口门上方的相机拍摄的。相机距离地面 3 米，位置可以拍摄奶牛的后、背骨盆和腰部区域的图像。两名技术人员在现场对每头奶牛的 BCS 进行估计，并将其与奶牛图像相关联。基准数据集包含 286 张带有关联 BCS、解剖点和形状的图像。它用于定量评估。创建了一组示例奶牛体型。线性和多项式核主成分分析用于使用从示例数据库构建的基本形状的线性组合来重建奶牛的形状。以这种方式，通过考虑她与平均形状的可变性来描述奶牛的体型。该方法产生了一个紧凑的形状描述，用于自动估计 BCS。模型验证表明，与其他最先进的方法相比，本研究提出的多项式模型在估计 BCS 时表现更好（误差=0.31），即使在 BCS 尺度的极值处也是如此。
