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.

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.

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.