In order to solve the problems of low efficiency and subjectivity of manual observation in the process of group-sheep-aggression detection, we propose a video streaming-based model for detecting aggressive behavior in group sheep. In the experiment, we collected videos of the sheep’s daily routine and videos of the aggressive behavior of sheep in the sheep pen. Using the open-source software LabelImg, we labeled the data with bounding boxes. Firstly, the YOLOv5 detects all sheep in each frame of the video and outputs the coordinates information. Secondly, we sort the sheep’s coordinates using a sheep tracking heuristic proposed in this paper. Finally, the sorted data are fed into an LSTM framework to predict the occurrence of aggression. To optimize the model’s parameters, we analyze the confidence, batch size and skipping frame. The best-performing model from our experiments has 93.38% Precision and 91.86% Recall. Additionally, we compare our video streaming-based model with image-based models for detecting aggression in group sheep. In sheep aggression, the video stream detection model can solve the false detection phenomenon caused by head impact feature occlusion of aggressive sheep in the image detection model.

In order to address the problems of large chop length, high energy consumption and poor chopping effect that were associated with a horizontal total mixed rations (TMR) mixer, a straw chopping device was designed; further, its structure and working principle were analyzed. In this study, wheat straw was chosen as the test object. The number of processing times, number of fixed blades, cutting speed, number of tooth plates and the inclined angle of the box were considered as the test factors. The standard straw length rate and average power were used as evaluation indices for conducting the orthogonal rotation combination experiment with five factors and five levels of quadratic regression. The experimental results denoted that the order of influencing factors on the standard straw length rate can be given as follows: the number of processing times, number of fixed blades, cutting speed, number of tooth plates and inclined angle of the box. The order of the influence of each factor on the average power can be given as follows: the cutting speed, number of fixed blades and number of tooth plates. Further, the optimum structural and working parameters of the device can be determined as follows: linear cutting speed of 17-20 m/min, box inclined angle of 70°-80°, 20-23 fixed blades, 5-6 processing times and 4-7 tooth plates. Under such a circumstance, the standard straw length rate reached up to 70.5%-77.4% and the average power was low at 5.8-7 kW. The results can be used as reference for designing a chopping device for a TMR mixer. Keywords: total mixed rations, straw, chopping device, parameter optimization DOI: 10.25165/j.ijabe.20201301.4927 Citation: Ma P B, Li L Q, Wen B Q, Xue Y H, Kan Z, Li J B. Design and parameter optimization of spiral-dragon type straw chopping test rig. Int J Agric & Biol Eng, 2020; 13(1): 47–56.

Sheep aggression detection is crucial for maintaining the welfare of a large-scale sheep breeding environment. Currently, animal aggression is predominantly detected using image and video detection methods. However, there is a lack of lightweight network models available for detecting aggressive behavior among groups of sheep. Therefore, this paper proposes a model for image detection of aggression behavior in group sheep. The proposed model utilizes the GhostNet network as its feature extraction network, incorporating the PWConv and Channel Shuffle operations into the GhostConv module. These additional modules improve the exchange of information between different feature maps. An ablation experiment was conducted to compare the detection effectiveness of the two modules in different positions. For increasing the amount of information in feature maps of the GhostBottleneck module, we applied the Inverted-GhostBottleneck module, which introduces inverted residual structure based on GhostBottleneck. The improved GhostNet lightweight feature extraction network achieves 94.7% Precision and 90.7% Recall, and its model size is only 62.7% of YOLOv5. Our improved model surpasses the original model in performance. Furthermore, it addresses the limitation of the video detection model, which was unable to accurately locate aggressive sheep. In real-time, our improved model successfully detects aggressive behavior among group sheep.