Thermal Imaging and Dimensionality Reduction Techniques for Subclinical Mastitis Detection in Dairy Sheep.

Simple Summary: Subclinical mastitis is a common and economically significant disease that affects dairy sheep production. This study aims to develop and evaluate an advanced algorithmic approach that integrates thermal imaging, statistical texture analysis, and t-distributed stochastic neighbor embedding (t-SNE) to accurately detect subclinical mastitis in dairy sheep. This approach focuses on improving the accuracy and reliability of non-invasive subclinical mastitis detection by using more sophisticated algorithmic procedures than traditional temperature differential methods, thereby enhancing livestock management and animal health monitoring. Subclinical mastitis is a common and economically significant disease that affects dairy sheep production. Thermal imaging presents a promising avenue for non-invasive detection, but existing methodologies often rely on simplistic temperature differentials, potentially leading to inaccurate assessments. This study proposes an advanced algorithmic approach integrating thermal imaging processing with statistical texture analysis and t-distributed stochastic neighbor embedding (t-SNE). Our method achieves a high classification accuracy of 84% using the support vector machines (SVM) algorithm. Furthermore, we introduce another commonly employed evaluation metric, correlating thermal images with commercial California mastitis test (CMT) results after establishing threshold conditions on statistical features, yielding a sensitivity (the true positive rate) of 80% and a specificity (the true negative rate) of 92.5%. The evaluation metrics underscore the efficacy of our approach in detecting subclinical mastitis in dairy sheep, offering a robust tool for improved management practices. [ABSTRACT FROM AUTHOR]