Feighelstein Marcelo, Mishael Amir, Malka Tamir, Magana Jennifer, Gavojdian Dinu, Zamansky Anna, Adams-Progar Amber
University of Haifa, Haifa, Israel.
Technion,, Haifa, Israel.
Sci Rep. 2024 Dec 2;14(1):29849. doi: 10.1038/s41598-024-80902-4.
Digital dermatitis (DD) is a common foot disease that can cause lameness, decreased milk production and fertility decline in cows. The prediction and early detection of DD can positively impact animal welfare and profitability of the dairy industry. This study applies deep learning-based computer vision techniques for early onset detection and prediction of DD using infrared thermography (IRT) data. We investigated the role of various inputs for these tasks, including thermal images of cow feet, statistical color features extracted from IRT images, and manually registered temperature values. Our models achieved performances of above 81% accuracy on DD detection on 'day 0' (first appearance of clinical signs), and above 70% accuracy prediction of DD two days prior to the first appearance of clinical signs. Moreover, current findings indicate that the use of IRT images in conjunction with AI based predictors show real potential for developing future real-time automated tools to monitoring DD in dairy cows.
数字皮炎（DD）是一种常见的足部疾病，可导致奶牛跛行、产奶量下降和繁殖力降低。DD的预测和早期检测对动物福利和乳制品行业的盈利能力具有积极影响。本研究应用基于深度学习的计算机视觉技术，利用红外热成像（IRT）数据对DD进行早期发病检测和预测。我们研究了各种输入在这些任务中的作用，包括牛蹄的热图像、从IRT图像中提取的统计颜色特征以及手动记录的温度值。我们的模型在DD检测的“第0天”（临床症状首次出现）时准确率达到81%以上，在临床症状首次出现前两天对DD的预测准确率达到70%以上。此外，目前的研究结果表明，将IRT图像与基于人工智能的预测器结合使用，在开发未来用于监测奶牛DD的实时自动化工具方面具有真正的潜力。
