A computer vision approach to improving cattle digestive health by the monitoring of faecal samples.
A computer vision approach to improving cattle digestive health by the monitoring of faecal samples.
复制标题
通过监测粪便样本改善牛消化系统健康的计算机视觉方法。
DOI：
10.1038/s41598-020-74511-0
复制
发表时间：
2020-10-16
影响因子：
4.6
通讯作者：
Kingston-Smith AH
中科院分区：
文献类型：
--
作者：
Atkinson GA;Smith LN;Smith ML;Reynolds CK;Humphries DJ;Moorby JM;Leemans DK;Kingston-Smith AH
奶牛的消化系统健康是决定其健康和生产力的主要因素之一。在牛肉和乳制品行业，喂养不足和过度都很常见；导致福利问题、负面环境影响和经济损失。不幸的是，由于许多因素，包括需要将粪便样本运送到实验室进行成分分析，大型农场的农民很难对消化系统健康进行常规监测。本文介绍了一种基于计算机视觉的低成本、易于使用的成像设备来监测消化系统健康的新方法。该方法包括快速捕获粪便样本的多个可见和近红外图像。然后，应用一种新颖的三维分析算法，根据样本的几何特征对其状况进行客观评分。虽然对结果的比较没有普遍的基础真理，但分数的顺序与定性的人类预测非常接近。该算法还能够使用深度学习方法检测未消化的纤维和玉米粒的存在。玉米和纤维在图像区域的检出率约为90%。这些结果表明，该系统有潜力在农场上开发，实时监测个体动物的消化健康，允许早期干预，有效地调整喂养策略。
The digestive health of cows is one of the primary factors that determine their well-being and productivity. Under- and over-feeding are both commonplace in the beef and dairy industry; leading to welfare issues, negative environmental impacts, and economic losses. Unfortunately, digestive health is difficult for farmers to routinely monitor in large farms due to many factors including the need to transport faecal samples to a laboratory for compositional analysis. This paper describes a novel means for monitoring digestive health via a low-cost and easy to use imaging device based on computer vision. The method involves the rapid capture of multiple visible and near-infrared images of faecal samples. A novel three-dimensional analysis algorithm is then applied to objectively score the condition of the sample based on its geometrical features. While there is no universal ground truth for comparison of results, the order of scores matched a qualitative human prediction very closely. The algorithm is also able to detect the presence of undigested fibres and corn kernels using a deep learning approach. Detection rates for corn and fibre in image regions were of the order 90%. These results indicate the potential to develop this system for on-farm, real time monitoring of the digestive health of individual animals, allowing early intervention to effectively adjust feeding strategy.
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DOI：
10.1109/34.3909
发表时间：
1988-07-01
影响因子：
23.6
作者：
FRANKOT, RT;CHELLAPPA, R
通讯作者：
CHELLAPPA, R
DOI：
10.1109/tpami.2019.2910523
发表时间：
2020-09-01
影响因子：
23.6
作者：
Lathuiliere, Stephane;Mesejo, Pablo;Horaud, Radu
通讯作者：
Horaud, Radu
影响因子：
3.5
作者：
Kertz, AF
通讯作者：
Kertz, AF
影响因子：
1.3
作者：
WOODHAM, RJ
通讯作者：
WOODHAM, RJ
影响因子：
3.5
作者：
IRELANDPERRY, RL;STALLINGS, CC
通讯作者：
STALLINGS, CC