In china, there is a great variety of sheep production systems, from the most traditional grazing types, mixed (grazing/stabling) to the most technologically advanced facility sheep. Body size parameters can reflect its growth development, production performance and genetic characteristics. So, monitoring body size is very important. In view of the problems or limitations of the present manual measurement, using the tools of measuring stick, tape measure, etc., the sheep having to stand on a flat place with correct posture, a non-contact method for measuring body dimensions of small-tailed Han sheep based on machine vision has been proposed and discussed. This approach is based on a position limit apparatus, computer-assisted visual capture, an automatic foreground extraction algorithm and a measuring point detection algorithm. The measured body sizes include withers height, back height, rump height, body length, chest depth, chest width, abdominal width and rump width. This approach has been examined in a specific farm for a case study. The errors of more than 90% measurements for sheep body size are within 3%. The results indicate that the method based on visual image analysis is effective, and it is especially suitable for the sheep feeding in an intensive and large-scale way.

In the era of globalization and digitization of livestock markets, sheep are considered an essential source of food production worldwide. However, sheep behavior monitoring, disease prevention, and precise management pose urgent challenges in the development of smart ranches. To address these problems, individual identification of sheep has become an increasingly viable solution. Despite the benefits of traditional sheep individual identification methods, such as accurate tracking and record-keeping, they are labor-intensive and inefficient. Popular convolutional neural networks (CNNs) are unable to extract features for specific problems, further complicating the issue. To overcome these limitations, an Attention Residual Module (ARM) is proposed to aggregate the feature mapping between different layers of the CNN. This approach enables the general model of the CNN to be more adaptable to task-specific feature extraction. Additionally, a targeted sheep face recognition dataset containing 4490 images of 38 individual sheep has been constructed. Furthermore, the experimental data was expanded using image enhancement techniques such as rotation and panning. The results of the experiments indicate that the accuracy of the VGG16, GoogLeNet, and ResNet50 networks with the ARM improved by 10.2%, 6.65%, and 4.38%, respectively, compared to these recognition networks without the ARM. Therefore, the proposed method for specific sheep face recognition tasks has been proven effective.

The study of the self-protective behaviors of dairy cows suffering dipteral insect infestation is important for evaluating the breeding environment and cows’ selective breeding. The current practices for measuring diary cows’ self-protective behaviors are mostly by human observation, which is not only tedious but also inefficient and inaccurate. In this paper, we develop an automatic monitoring system based on video analysis. First, an improved optical flow tracking algorithm based on Shi-Tomasi corner detection is presented. By combining the morphological features of head, leg, and tail movements, this method effectively reduces the number of Shi-Tomasi points, eliminates interference from background movement, reduces the computational complexity of the algorithm, and improves detection accuracy. The detection algorithm is used to calculate the number of tail, leg, and head movements by using an artificial neural network. The accuracy range of the tail and head reached [0.88, 1] and the recall rate was [0.87, 1]. The method proposed in this paper which provides objective measurements can help researchers to more effectively analyze dairy cows’ self-protective behaviors and the living environment in the process of dairy cow breeding and management.

The mutton sheep breeding industry has transformed significantly in recent years, from traditional grassland free-range farming to a more intelligent approach. As a result, automated sheep face recognition systems have become vital to modern breeding practices and have gradually replaced ear tagging and other manual tracking techniques. Although sheep face datasets have been introduced in previous studies, they have often involved pose or background restrictions (e.g., fixing of the subject’s head, cleaning of the face), which restrict data collection and have limited the size of available sample sets. As a result, a comprehensive benchmark designed exclusively for the evaluation of individual sheep recognition algorithms is lacking. To address this issue, this study developed a large-scale benchmark dataset, Sheepface-107, comprising 5350 images acquired from 107 different subjects. Images were collected from each sheep at multiple angles, including front and back views, in a diverse collection that provides a more comprehensive representation of facial features. In addition to the dataset, an assessment protocol was developed by applying multiple evaluation metrics to the results produced by three different deep learning models: VGG16, GoogLeNet, and ResNet50, which achieved F1-scores of 83.79%, 89.11%, and 93.44%, respectively. A statistical analysis of each algorithm suggested that accuracy and the number of parameters were the most informative metrics for use in evaluating recognition performance.

A high update rate is always one of the vital indices of star trackers. By recording star positions at N moments within a single star image, the multi-exposure imaging approach (MEIA) proposed in an earlier study can improve the attitude update rate of star trackers by N times. Unfortunately, when the existing star matching method is adopted to match the observed and predicted stars in MEIA, the matching time is significantly increased with the increase in multi-exposure times, N, or the number of navigation stars, M, which sharply affects the MEIA’s performance. Therefore, a fast star matching method based on double K-vector lookup tables (DKVLUTs) is proposed to address the above issue. In this method, the information of all predicted stars is used to establish the DKVLUT, and thus, the speed of the whole matching process between observed and predicted stars would be increased effectively by means of the DKVLUT. Both simulations and experiments are conducted to verify the performance of the proposed method. The results both show that the matching time of the proposed method is reduced by nearly one order of magnitude compared with that of the existing method, which demonstrates the feasibility and effectiveness of the proposed method.

The mutton sheep breeding industry has transformed significantly in recent years, from traditional grassland free-range farming to a more intelligent approach. As a result, automated sheep face recognition systems have become vital to modern breeding practices and have gradually replaced ear tagging and other manual tracking techniques. Although sheep face datasets have been introduced in previous studies, they have often involved pose or background restrictions (e.g., fixing of the subject’s head, cleaning of the face), which restrict data collection and have limited the size of available sample sets. As a result, a comprehensive benchmark designed exclusively for the evaluation of individual sheep recognition algorithms is lacking. To address this issue, this study develops a large-scale benchmark dataset, Sheepface-107, comprised of 5,350 images acquired from 107 different subjects. Images were collected from each sheep at multiple angles, including front and back views, in a diverse collection that provides a more comprehensive representation of facial features. In addition to the dataset, an assessment protocol is developed by applying multiple evaluation metrics to the results produced by three different deep learning models: VGG16, GoogLeNet, and ResNet50, which achieved F1-scores of 83.79%, 89.11%, and 93.44%, respectively. A statistical analysis of each algorithm suggested that accuracy and the number of parameters were the most informative metrics for use in evaluating recognition performance.