Research progress of the recognition of free-range sheep behavior using sensor technology

An accurate and rapid recognition of the behavior of sheep can fully reflect the internal physiological state and health level for the better welfare of livestock breeding. At the same time, the health status of grazing grassland can also be analyzed to combine with the location characteristics. Then, the grass condition can be grasped in time to ensure the balance and sustainable development of grass and livestock in the grassland ecosystem. However, it is very difficult to monitor the behavior of free-grazing sheep under the influence of spatial scale. The computer vision with high accuracy is also inconvenient to arranging the equipment and facilities, and then collecting data under the harsh field environment and vast area, compared with the indoor intensive farming mode. The traditional manual observation cannot fully meet the requirement of large-scale production at this stage, due to the low efficiency and high subjectivity. Contact sensors can be expected to identify the behavior of sheep for the relationship between sheep and grassland environment, particularly with the rapid development of key technologies, such as communication and big data. Behavior monitoring included sustained behavior (grazing, walking, ruminating, running, and resting), and transient behavior (parturition, estrus, and urination) recognition. A systematic analysis has been implemented to determine the feed intake distribution, feeding intensity distribution, grazing intensity, and the spatiotemporal change of sheep in the field of grassland. A theoretical basis and technical means can be provided for the formulation of a grazing system in the monitoring of livestock and grass conditions for accurate animal husbandry. In this review, three commonly-used sensor technologies were introduced in sheep behavior monitoring at present. Data acquisition, processing, feature extraction, and modelling were also analyzed to summarize the existing methods and challenges in the key technologies. The research object was mainly for the large ruminants, where the research on sheep behavior monitoring was still in its infancy. There were many similarities between sheep and cattle, as the main grazing livestock in grassland. There were still some differences to need further exploration. Tri-Axis acceleration sensors were selected to compare the different deployment positions and time intervals. Appropriate positions and time intervals were then optimized, according to the target behavior combined with the grassland condition. However it is still lacking to consider the sampling frequency. The satellite signal was generally blocked by some obstacles in the condition of free grazing, which was easy to cause data loss. Current imaging data was collected from the artificial pastures with the small area, in order to simulate the living environment of sheep. But there were still differences from the real complex pastures. It was difficult to replace the equipment in the vast pasture area as the survival basis for sheep grazing. Frequent equipment replacement should be given special attention to reducing human participation in intelligent grazing management. In addition, the application status of key technologies in the different behaviors was illustrated in combination with the behavior pattern of sheep. Finally, the development direction of the contact sensor was proposed in the future. Firstly, the multi-sensor fusion system can be combined as complementary, according to the characteristics of the sensor, in order to more accurately infer the behavior of sheep, and then assess the health status of grazing pasture. Secondly, a deep learning network can be utilized to analyze the image data, in order to dig deeper and then distinguish the feature information. The manual extraction of features can be reduced to overcome the data imbalance and insufficient data for the recognition of complex patterns. A lightweight end-to-end network model was established for deployment in the embedded systems or mobile devices, fully meeting the practical applications.