The aim of this study was to employ machine learning algorithms based on sensor behavior data for (1) early-onset detection of digital dermatitis (DD); and (2) DD prediction in dairy cows. With the ultimate goal to set-up early warning tools for DD prediction, which would than allow a better monitoring and management of DD under commercial settings, resulting in a decrease of DD prevalence and severity, while improving animal welfare. A machine learning model that is capable of predicting and detecting digital dermatitis in cows housed under free-stall conditions based on behavior sensor data has been purposed and tested in this exploratory study. The model for DD detection on day 0 of the appearance of the clinical signs has reached an accuracy of 79%, while the model for prediction of DD 2 days prior to the appearance of the first clinical signs has reached an accuracy of 64%. The proposed machine learning models could help to develop a real-time automated tool for monitoring and diagnostic of DD in lactating dairy cows, based on behavior sensor data under conventional dairy environments. Results showed that alterations in behavioral patterns at individual levels can be used as inputs in an early warning system for herd management in order to detect variances in health of individual cows.

本研究旨在利用基于传感器行为数据的机器学习算法，早期检测数字皮炎(DD)并预测奶牛DD。通过建立早期警报工具，实现对商业环境下DD的监测和管理，降低DD的发病率和严重程度，改善动物福利。通过基于行为传感器数据的机器学习模型，在自由栏条件下预测和检测奶牛数字皮炎，本探索性研究中，DD临床症状出现的第0天的检测模型准确率达到79%，DD临床症状出现前2天的预测模型准确率达到64%。该机器学习模型有助于基于行为传感器数据在常规奶牛环境下开发实时自动化的DD监测和诊断工具。研究结果表明，个体层面的行为模式变化可用作牛群管理早期警报系统的输入，以检测个体牛健康的差异。

使用传感器数据的机器学习方法预测和检测奶牛数字皮炎早期发作