Artificial Intelligence Topping on Spectral Analysis for Lameness Detection in Dairy Cattle

抄録

Artificial Intelligence is exponentially evolving into a solution to many life science complex problems especially human and animal healthcare systems. On the other hand, Spectral Analysis tools are coming to the front lines in processing image signals. These days, researchers and the industry have focused on image signal processing techniques to support farmers in automatically finding lame cows in their dairy farms. Lameness in dairy cattle is the number one welfare issue in the dairy industry due to pain, suffering, and economic impact. Therefore, in this paper, we propose a spectral analysis embedded Artificial Intelligence approach to cattle lameness detection by investigating and analyzing the image depth signals taken on the individual dairy cows while they are walking in the pathways from the milking station to resting areas. Specifically, we shall first develop the frequency signal variation measures of the collected image depth signals of individual cows by using spectral analysis. Then some AI models will be used to analyze obtained frequency variation measures for detecting cattle lameness scores. Finally, we present some partial experimental results using self-collected real-life data.