Song X, van Mourik S, Bokkers E A M, Groot Koerkamp P W G, van der Tol P P J
Farm Technology Group, Wageningen University and Research, PO Box 16, Wageningen, 6700 AA, the Netherlands.
Smart Component Department, Lely Industries N.V., Cornelis van der Lelylaan 1, Maassluis, 3147 PB, the Netherlands.
JDS Commun. 2022 Feb 10;3(2):126-131. doi: 10.3168/jdsc.2021-0165. eCollection 2022 Mar.
A 3-dimensional (3D) vision-based system was previously designed to automatically estimate the rumen motility of individual cows. This longitudinal study aimed to explore the associations between 3D vision-based rumen function assessment and dairy cow feed changes and milk production on a commercial farm. The 3D vision system was attached to an automatic milking robot to estimate the ruminal contraction frequency and rumen fill in 42 lactating cows during each milking event for 66 d. Additionally, we collected data on milk production, milk composition, general health, and changes in feeding practices. The 3D vision system showed that half the cows displayed a drastic decrease in the estimated rumen fill when all cows began grazing. The grazing and decreased rumen fill were also associated with herd-level milk fat depression. Over the 66 d, one cow was detected with reduced milk production and suspected rumen dysfunction by the farmer. The 3D vision system, however, identified this cow as having sudden decreases in estimated ruminal contraction frequency and rumen fill 4 d before detection by the farmer. In this longitudinal study, the 3D vision-based rumen function assessment system showed potential as a useful management-supporting tool for dairy farmers. The system, however, requires further validation with more cows of various breeds and ages. We suggest validating the 3D vision system with rumen boluses, quantified adjustments in feeding practices, more cases with ruminal dysfunction, and systematic health assessments for future studies.
先前设计了一种基于三维（3D）视觉的系统，用于自动估计个体奶牛的瘤胃蠕动。这项纵向研究旨在探讨基于3D视觉的瘤胃功能评估与商业农场中奶牛饲料变化及产奶量之间的关联。将3D视觉系统安装在自动挤奶机器人上，在66天的每次挤奶过程中估计42头泌乳奶牛的瘤胃收缩频率和瘤胃充盈度。此外，我们收集了产奶量、牛奶成分、总体健康状况以及饲养方式变化的数据。3D视觉系统显示，当所有奶牛开始放牧时，一半的奶牛估计瘤胃充盈度急剧下降。放牧和瘤胃充盈度下降还与牛群水平的乳脂降低有关。在66天的时间里，农民检测到一头奶牛产奶量下降且怀疑有瘤胃功能障碍。然而，3D视觉系统在农民检测到的4天前就识别出这头奶牛的估计瘤胃收缩频率和瘤胃充盈度突然下降。在这项纵向研究中，基于3D视觉的瘤胃功能评估系统显示出作为奶农有用的管理支持工具的潜力。然而，该系统需要用更多不同品种和年龄的奶牛进行进一步验证。我们建议在未来的研究中用瘤胃丸剂、饲养方式的量化调整、更多瘤胃功能障碍病例以及系统的健康评估来验证3D视觉系统。
