Cows mounting behavior is a significant manifestation of estrus in cows. The timely detection of cows mounting behavior can make cows conceive in time, thereby improving milk production of cows and economic benefits of the pasture. Existing methods of mounting behavior detection are difficult to achieve precise detection under occlusion and severe scale change environments and meet real-time requirements. Therefore, this study proposed a Cow-YOLO model to detect cows mounting behavior. To meet the needs of real-time performance, YOLOv5s model is used as the baseline model. In order to solve the problem of difficult detection of cows mounting behavior in an occluded environment, the CSPDarknet53 of YOLOv5s is replaced with Non-local CSPDarknet53, which enables the network to obtain global information and improves the model’s ability to detect the mounting cows. Next, the neck of YOLOv5s is redesigned to Multiscale Neck, reinforcing the multi-scale feature fusion capability of model to solve difficulty detection under dramatic scale changes. Then, to further increase the detection accuracy, the Coordinate Attention Head is integrated into YOLOv5s. Finally, these improvements form a novel cow mounting detection model called Cow-YOLO and make Cow-YOLO more suitable for cows mounting behavior detection in occluded and drastic scale changes environments. Cow-YOLO achieved a precision of 99.7%, a recall of 99.5%, a mean average precision of 99.5%, and a detection speed of 156.3 f/s on the test set. Compared with existing detection methods of cows mounting behavior, Cow-YOLO achieved higher detection accuracy and faster detection speed in an occluded and drastic scale-change environment. Cow-YOLO can assist ranch breeders in achieving real-time monitoring of cows estrus, enhancing ranch economic efficiency.

High precision pig cough recognition and low computational cost is of great importance for the realization of early warning of pig respiratory diseases. Numerous researchers have improved the recognition rate of pig cough sounds to a certain extent from feature selection and feature fusion perspectives. However, there is still a margin for the improvement in the accuracy and complexity of existing methods. Meanwhile, it is challenging to further enhance the precision of a single classifier. Therefore, this study proposed a multi-classifier fusion strategy based on Dempster Shafer distance (DS-distance) algorithm to increase the classification accuracy. Considering the engineering implementation, the machine learning with low computational complexity for fusion was chosen. First, three metrics of accuracy and diversity between classifiers were defined, including overall accuracy (OA), double fault (DF), and overall accuracy and double fault (OADF), for selecting the base classifiers. Subsequently, a two-step base classifier selection approach based on these metrics was proposed to make an optimized selection of features and classifiers. Finally, the proposed DS-distance algorithm was used to fuse the selected base classifiers to create a classification. The sound data collected in the pig barn verified the proposed algorithm. The experimental results revealed that the overall recognition accuracy of the proposed method could reach 98.76%, which was better than the existing methods. This study has achieved a high recognition accuracy through ensembled machine learning with low computational complexity. The proposed method provided an efficient way for the quick establishment of high precision pig cough recognition model in practice.