Multi-feature image layers fusion for accurate detection of dairy cow mastitis using deep learning

Mastitis in dairy cows is a costly disease that poses significant challenges to animal welfare and farm productivity. Traditional detection methods often rely on single feature and simple thresholding or machine learning algorithms, susceptible to environmental factors and individual cow specificity. To address these limitations, we proposed an automatic mastitis detection method based on the fusion of multiple feature image layers derived from thermal infrared imaging. First, thermal images of the udder region were captured to extract three distinct feature image layers, including temperature distribution, vascular structure, and udder size. The temperature layer captured the thermal variations of the udder and was generated by producing temperature contour plots from the raw images. The blood vessel layer highlighted vascular patterns derived from Laplacian filtering and skeletonization of the extracted udder region. The size layer provided measurements of udder dimensions, which were automatically detected using the CenterNet model and fitted with corresponding keypoints. These feature layers integrated both sides of the cow’s udder to create a dual-channel composite feature image. Finally, the DenseNet-201 deep learning model was employed to classify mastitis within a dataset of 7,000 thermal images. The proposed method achieved a classification accuracy of 91.88%, significantly outperforming the 78.60% accuracy obtained using only raw thermal images. Furthermore, ablation studies were conducted to assess the contributions of each feature layer to overall detection performance. The results demonstrate that multi-feature fusion achieved significantly superior performance to single features. Such an investigation provides a robust solution for early mastitis detection. It eliminates the requirements of manual feature extraction and temperature differential calculations, paving the way for automated animal health monitoring and unmanned farm management.