A5037571961- Animal Behavior and Welfare Studies
- Effects of Environmental Stressors on Livestock
- Video Surveillance and Tracking Methods
- Smart Agriculture and AI
- Textile materials and evaluations
- Industrial Vision Systems and Defect Detection
- Air Quality Monitoring and Forecasting
- Genetic and phenotypic traits in livestock
- Meat and Animal Product Quality
- Odor and Emission Control Technologies
- Animal Disease Management and Epidemiology
Xinjiang Production and Construction Corps
2022-2023
Zhongkai University of Agriculture and Engineering
2022-2023
Shihezi University
2022-2023
Ministry of Agriculture and Rural Affairs
2023
There are some problems with estrus detection in ewes large-scale meat sheep farming: mainly, the manual method is labor-intensive and contact sensor causes stress reactions ewes. To solve abovementioned problems, we proposed a multi-objective layer neural network-based for ewe crawling behavior recognition. The approach has four main parts. Firstly, to address problem of mismatch between our constructed dataset YOLO v3 anchor box size, propose obtain new size by clustering using K-means++...
We propose a lightweight neural network-based method to detect the estrus behavior of ewes. Our suggested is mainly proposed solve problem not being able ewe in timely and accurate manner large-scale meat sheep farms. The three main steps our methodology include constructing dataset, improving network structure, detecting based on network. First, dataset was constructed by capturing images from videos with crawling behavior, data enhancement performed improve generalization ability model at...
In order to solve the problems of low efficiency and subjectivity manual observation in process group-sheep-aggression detection, we propose a video streaming-based model for detecting aggressive behavior group sheep. experiment, collected videos sheep's daily routine sheep pen. Using open-source software LabelImg, labeled data with bounding boxes. Firstly, YOLOv5 detects all each frame outputs coordinates information. Secondly, sort using tracking heuristic proposed this paper. Finally,...
To solve the problems of high labor intensity, low efficiency, and frequent errors in manual identification cone yarn types, this study five kinds were taken as research objects, an method for based on improved Faster R-CNN model was proposed. In total, 2750 images collected samples real textile industry environments, then data enhancement performed after marking targets. The ResNet50 with strong representation ability used feature network to replace VGG16 backbone original extract features...
In large-scale meat sheep farming, high CO2 concentrations in sheds can lead to stress and harm the healthy growth of sheep, so a timely accurate understanding trend concentration early regulation are essential ensure environmental safety welfare sheep. order accurately understand regulate barns, we propose prediction method based on RF-PSO-LSTM model. The approach has four main parts. First, address problems data packet loss, distortion, singular values, differences magnitude ambient air...
Sheep aggression detection is crucial for maintaining the welfare of a large-scale sheep breeding environment. Currently, animal predominantly detected using image and video methods. However, there lack lightweight network models available detecting aggressive behavior among groups sheep. Therefore, this paper proposes model in group The proposed utilizes GhostNet as its feature extraction network, incorporating PWConv Channel Shuffle operations into GhostConv module. These additional...