Ding Han

As technology advances, rangeland management is rapidly transitioning toward intelligent systems. To optimize grassland resources and implement scientific grazing practices, livestock grazing monitoring has become a pivotal area of research. Traditional methods, such as manual tracking and wearable monitoring, often disrupt the natural movement and feeding behaviors of grazing livestock, posing significant challenges for in-depth studies of grazing patterns. In this paper, we propose a machine vision-based grazing goat herd detection algorithm that enhances the streamlined ELAN module in YOLOv7-tiny, incorporates an optimized CBAM attention mechanism, refines the SPPCSPC module to reduce the parameter count, and improves the anchor boxes in YOLOv7-tiny to enhance target detection accuracy. The BiAF-YOLOv7 algorithm achieves precision, recall, F1 score, and mAP values of 94.5, 96.7, 94.8, and 96.0%, respectively, on the goat herd dataset. Combined with DeepSORT, our system successfully tracks goat herds, demonstrating the effectiveness of the BiAF-YOLOv7 algorithm as a tool for livestock grazing monitoring. This study not only validates the practicality of the proposed algorithm but also highlights the broader applicability of machine vision-based monitoring in large-scale environments. It provides innovative approaches to achieve grass-animal balance through information-driven methods, such as monitoring and tracking.

According to related research, different body temperatures, heart rates, and locomotor behaviors of small-tailed cold goats can represent the physical condition of the goats themselves and are used as direct evidence for evaluating the physical health status of small-tailed cold goats. In this paper, we designed and tested a system for predicting the health status of small-tailed cold sheep based on wearable information monitoring technology. To test the system, sheep wearable devices were worn on 36 small-tailed cold sheep of different ages and inconsistent health conditions at different time points from May to October. A SLBAS-BP neural network model for predicting the health condition of small-tailed cold sheep was established using the collected and processed data, which overcame the problem that the traditional gradient descent method in the BP neural network is prone to fall into local optimization leading to insufficient prediction ability. The correct prediction rates of the improved BP neural network for the four health conditions of healthy, sub-healthy, fever, and disease were 98.4%, 94.5%, 90.4%, and 98.7%, respectively, and the average correct prediction rate of the four conditions was 5.8% higher than that before the improvement, reaching 95.2%.

In order to achieve goat localization to help prevent goats from wandering, we proposed an efficient target localization method based on machine vision. Albas velvet goats from a farm in Ertok Banner, Ordos City, Inner Mongolia Autonomous Region, China, were the main objects of study. First, we proposed detecting the goats using a shallow convolutional neural network, ShallowSE, with the channel attention mechanism SENet, the GeLU activation function and layer normalization. Second, we designed three fully connected coordinate regression network models to predict the spatial coordinates of the goats. Finally, the target detection algorithm and the coordinate regression algorithm were combined to localize the flock. We experimentally confirmed the proposed method using our dataset. The proposed algorithm obtained a good detection accuracy and successful localization rate compared to other popular algorithms. The overall number of parameters in the target detection algorithm model was only 4.5 M. The average detection accuracy reached 95.89% and the detection time was only 8.5 ms. The average localization error of the group localization algorithm was only 0.94 m and the localization time was 0.21 s. In conclusion, the method achieved fast and accurate localization, which helped to rationalize the use of grassland resources and to promote the sustainable development of rangelands.

Aiming at the problem of time-consuming, labor-intensive, and low-accuracy monitoring of goat motion behavior (lying, standing, walking, and running) while relying on the three-axis acceleration sensor and taking the acceleration data obtained from the goat back collection point as the research object, a method based on social learning (SL) is proposed using the Whale Optimization Algorithm (WOA) and XGBoost for goat motion behavior recognition. In this method, the XGBoost parameters are optimized by the WOA combined with social learning strategies to improve the classification and recognition accuracy. The results show that the recognition rate of lying behavior was as high as 97.14%, and the average recognition rate of the four movement behaviors was 94.42%, meeting the requirements of goat motion behavior recognition. Compared with the conventional XGBoost algorithm, the average recognition rate was increased by 3.41% and the recognition accuracy was improved. The results of this study can provide a reference for goat health assessment and intelligent disease warning.

With the advancement of deep learning technology, the importance of utilizing deep learning for livestock management is becoming increasingly evident. goat face detection provides a foundation for goat recognition and management. In this study, we proposed a novel neural network specifically designed for goat face object detection, addressing challenges such as low image resolution, small goat face targets, and indistinct features. By incorporating contextual information and feature-fusion complementation, our approach was compared with existing object detection networks using evaluation metrics such as F1-Score (F1), precision (P), recall (R), and average precision (AP). Our results show that there are 8.07%, 0.06, and 6.8% improvements in AP, P, and R, respectively. The findings confirm that the proposed object detection network effectively mitigates the impact of small targets in goat face detection, providing a solid basis for the development of intelligent management systems for modern livestock farms.

This paper proposes a design method for a channel-type sheep dynamic weighing system to address the current problems encountered by pastoralists at home and abroad, such as time-consuming sheep weighing, difficulties with data collection, and management of the stress response in sheep. The complete system includes a hardware structure, dynamic characteristics, and a Kalman-aggregate empirical modal decomposition algorithm (Kalman-EEMD algorithm) model for dynamic data processing. The noise suppression effects of the Kalman filter, the empirical modal decomposition (EMD), and the ensemble empirical modal decomposition (EEMD) algorithms are discussed for practical applications. Field tests showed that the Kalman-EEMD algorithm model has the advantages of high accuracy, efficiency, and reliability. The maximum error between the actual weight of the goats and the measured value in the experiments was 1.0%, with an average error as low as 0.40% and a maximum pass time of 2 s for a single goat. This meets the needs for weighing accuracy and goat flock weighing rates.

Alignment technology plays an important role in navigation, and is used extensively throughout military and civilian applications. However, the existing in-flight alignment methods cannot be applied to the low-cost based strap-down inertial navigation system/global positioning system integrated navigation system, used in short-endurance and high-speed rotation flight bodies, since they cannot quickly obtain alignment results to meet the accuracy requirements of a flight body with special movement characteristics of short-endurance and high-speed rotation. In this paper, in order to solve this challenging problem of alignment for flight body with short-endurance and high-speed rotation, a fast in-flight alignment method based on the Lie group is proposed. First, an in-flight alignment model based on vector observations was established by using the Lie group. Second, addressing the problem that the alignment accuracy is greatly affected by the low-cost inertial sensor bias, an improved unscented Kalman filter was constructed in the Lie group on the basis of fully considering the characteristics of the system equations to estimate and feedback the correlated errors. Finally, a trajectory simulation of high-speed flight body and field semi-physical test was carried out to evaluate the proposed method. Evaluation of the system performance in comparison with existing state-of-the-art methods indicated that the proposed in-flight alignment method has better alignment accuracy and faster alignment velocity for a low-cost strap-down inertial navigation system/global positioning system integrated navigation system.