Spatio-Temporal-Based Identification of Aggressive Behavior in Group Sheep

SIMPLE SUMMARY: Artificial intelligence technology increases the level of awareness in sheep farming. The aggressive behavior of sheep is related to mortality, feed provisioning, and flock density, and can impact sheep welfare and business benefits in precision animal husbandry. Currently, animal behavior is mainly observed manually, leading to increased labor costs. Contact sensor methods not only increase breeding costs but also induce stress reactions in sheep. The non-contact computer vision method avoids the above problems. Therefore, we propose a deep learning model that combines machine vision and time series analysis. This model aims to meet the requirement of timely and accurate detection of aggressive behavior in large-scale sheep farming. ABSTRACT: 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.