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.

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%.