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.

In response to the complex animal husbandry environment, wide livestock range, and labor shortage in Inner Mongolia grasslands, this study designed an intelligent control system for robotic vehicles used in natural grazing grassland environments. The control system consists of software and hardware components and motion control algorithms. Based on the application characteristics of different mechanisms of the robotic vehicle, the motion control of the robotic vehicle is decomposed into longitudinal speed control and lateral steering control. The longitudinal speed control adopts the traditional Proportional-Integral-Differential (PID) control method. Since the hardware composition and internal angle calculation method of the steering system are complex and prone to deviations caused by external terrain, a lateral adaptive fuzzy PID controller was constructed with lateral deviation and deviation change rate as input variables and parameters $K_p$, $K_i$, and $K_d$ as outputs. The field test results show that when the longitudinal speed was set to 0.5 m/s, the adjustment time was 1.95 s, and the steady-state error was about 0. When the longitudinal speed was the same, the lateral adaptive fuzzy PID controller reduced the rise time by 0.77 s compared to traditional control methods, reduced overshoot and steady-state error by 2%, and quickly recovered to a steady state after being disturbed. The following test results show that the designed intelligent control system can achieve real-time tracking of targets and that the motion controllers can effectively control speed and steering angle. Therefore, the intelligent control system designed in this paper can effectively achieve stable and real-time control of the robotic vehicle.

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.