Riaz Muhammad Usman, O'Grady Luke, McAloon Conor G, Logan Finnian, Gormley Isobel Claire
School of Mathematics and Statistics, University College Dublin, Belfield, Dublin, Ireland.
School of Veterinary Medicine, University College Dublin, Belfield, Dublin, Ireland.
PLoS One. 2025 Jun 27;20(6):e0325927. doi: 10.1371/journal.pone.0325927. eCollection 2025.
Lameness is one of the major production diseases affecting dairy cattle. It is associated with negative welfare in affected cattle, economic losses at the farm level, and adverse effects on sustainability. Prompt identification of lameness is necessary to facilitate early treatment, enhance animal welfare, and mitigate short and long-term production impacts associated with the disease. In recent years, automated detection systems have emerged as a potential solution for identifying early signs of lameness. Among these systems, accelerometers have been widely adopted, as they continuously capture data on animal movement. Analyzing accelerometer data is challenging due to its wide, high-dimensional structure as it has many features and typically much fewer animals or samples, reducing the utility of many machine learning (ML) models and increasing the risk of overfitting. To handle this, researchers often summarize accelerometer data into indices like step counts, which simplifies analysis but may sacrifice important details needed for accurate prediction of lameness. Dimension reduction techniques, such as principal component analysis (PCA) and functional principal component analysis (fPCA), offer solutions by reducing the dimensionality of the data while retaining key information and allowing for the application of a broader set of ML approaches. Using data containing 20 thousand recordings from 383 dairy cows in 11 dairy herds, this study evaluated the effectiveness of ML methods in detecting foot lesions in dairy cows using accelerometer data, with a focus on dimensionality reduction approaches and cross-validation strategies. Our study offers practical insights for the dairy industry by highlighting the potential benefits of combining dimensionality reduction with cross-validation strategies to improve the performance of ML methods applied to wide accelerometer data. In addition, our study highlights the impact and importance of using data from independent farms. A by-farm approach to cross-validation will likely give a more robust, realistic estimate of general model performance.
跛行是影响奶牛的主要生产性疾病之一。它与患病奶牛的负面福利、农场层面的经济损失以及对可持续性的不利影响相关。及时识别跛行对于促进早期治疗、提高动物福利以及减轻与该疾病相关的短期和长期生产影响至关重要。近年来，自动检测系统已成为识别跛行早期迹象的一种潜在解决方案。在这些系统中，加速度计已被广泛采用，因为它们能持续捕捉动物运动数据。由于加速度计数据具有广泛的高维结构，具有许多特征，而动物或样本数量通常少得多，这使得分析加速度计数据具有挑战性，降低了许多机器学习（ML）模型的效用，并增加了过拟合的风险。为了解决这个问题，研究人员通常将加速度计数据汇总为步数等指标，这简化了分析，但可能会牺牲准确预测跛行所需的重要细节。降维技术，如主成分分析（PCA）和功能主成分分析（fPCA），通过降低数据维度同时保留关键信息，并允许应用更广泛的ML方法来提供解决方案。本研究使用来自11个奶牛场的383头奶牛的2万条记录数据，评估了ML方法在利用加速度计数据检测奶牛足部病变方面的有效性，重点关注降维方法和交叉验证策略。我们的研究通过强调将降维与交叉验证策略相结合以提高应用于广泛加速度计数据的ML方法性能的潜在好处，为乳制品行业提供了实际见解。此外，我们的研究突出了使用独立农场数据的影响和重要性。按农场进行交叉验证的方法可能会对一般模型性能给出更稳健、现实的估计。
