Systems and methods for detecting udder disease based on machine learning methods and complementary supporting techniques are presented. Included are methods for assembling time sequences of images of each animal of a herd or set for subsequent use in per-animal image analysis for disease detection. Methods presented also include image pre-processing methods used prior to image analysis, resulting in contrast and resolution optimization such as appropriate image intensity level adjust wilt and resolution down sampling for more rapid and more accurate disease detection. Combinatorial techniques for compositing whole-udder images or udder-quarter images from partial images captures are described. Methods are provided for power usage optimization in regard to computing resources used in the computing-intensive AI analysis methods. Location-based and animal history-based detection refinements are incorporated into described systems. Further presented are methods for multi-modal and multi-factor detection of udder disease, as well as methods for infection type classification.
