The modern pig farming industry tends to scale up and intensify, and the aggressive behavior among gregarious pigs occurs frequently, which seriously affects their health and production, and economic benefit. An end-to-end anchor-free temporal localization framework is proposed to automatically detect aggressive behavior among gregarious pigs, its occurrence and their time periods by means of surveillance videos. The model first extracted the representative aggressive behavior from the surveillance video by I3D feature extraction network, and then input the features into the temporal pyramid network to obtain multi-scale temporal information, and finally used coarse prediction to obtain the initial nomination, and used fine prediction to refine the obtained coarse nomination, and the coarse prediction regressed the frame position of the action interval, the offset from the start and end of the action, and the occurrence of the action by the temporal convolution network. The fine prediction refined the boundary positions and obtained the final prediction results by activation-guided learning and boundary contrast learning. In order to train and validate the proposed model, a video dataset containing 174 videos of different durations and 464 temporal annotations for the detection of aggressive behavior among gregarious pigs was constructed. The experimental results showed that the model could achieve a recall rate of 79.1% at average tIoU when the number of nominations was 100, and detected 90 min of original surveillance video containing 10 segments of aggressive behaviors, and the prediction results could cover all real instances, which could better detect the aggressive behavior among gregarious pigs. This study can provide a reference for modern pig farms to achieve intelligent analysis and healthy breeding.

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.

As a major contributor to methane production in agriculture, there is a need for a suitable methane inhibitor to reduce ruminant methane emissions and minimize the impact on the climate. This work aimed to explore the influence of cordycepin on rumen fermentation, gas production, microbiome and their metabolites. A total of 0.00, 0.08, 0.16, 0.32, and 0.64 g L^–1 cordycepin were added into fermentation bottles containing 2 g total mixed ration for in vitro ruminal fermentation, and then the gas produced and fermentation parameters were measured for each bottle. Samples from the 0 and 0.64 g L^–1 cordycepin addition were selected for 16S rRNA gene sequencing and metabolome analysis. The result of this experiment indicated that the addition of cordycepin could linearly increase the concentration of total volatile fatty acid, ammonia nitrogen, the proportion of propionate, valerate, and isovalerate, and linearly reduce ruminal pH and methane, carbon dioxide, hydrogen and total gas production, as well as the methane proportion, carbon dioxide proportion and proportion of butyrate. In addition, there was a quadratic relationship between hydrogen and cordycepin addition. At the same time, the relative abundance of Succiniclasticum, Prevotella, Rikenellaceae_RC9_gut_group, NK4A214_group, Christensenellaceae_R_7_group, unclassified_F082, Veillonellaceae_UCG_001, Dasytricha, Ophryoscolex, Isotricha, unclassified_Eukaryota, Methanobrevibacter, and Piromyces decreased significantly after adding the maximum dose of cordycepin. In contrast, the relative abundance of Succinivibrio, unclassified_Succinivibrionaceae, Prevotellaceae_UCG_001, unclassified_Lachnospiraceae, Lachnospira, Succinivibrionaceae_UCG_002, Pseudobutyrivibrio, Entodinium, Polyplastron, unclassified_Methanomethylophilaceae, Methanosphaera, and Candidatus_Methanomethylophilus increased significantly. Metabolic pathways such as biosynthesis of unsaturated fatty acids and purine metabolism and metabolites such as arachidonic acid, adenine, and 2´-deoxyguanosine were also affected by the addition of cordycepin. Based on this, we conclude that cordycepin is an effective methane emission inhibitor that can change the rumen metabolites and fermentation parameters by influencing the rumen microbiome, thus regulating rumen methane production. This experiment may provide a potential theoretical reference for developing Cordyceps byproduct or additives containing cordycepin as methane inhibitors.