Authors

Abstract

Individual identification and behavioural analysis of pigs is a key link in the intelligent management of a piggery, for which the computer vision technology based on application and improvement of deep learning model has become the mainstream. However, the operation of the model has high requirements to hardwares, also the model is of weak interpretability, which make it difficult to adapt to both the mobile terminals and the embedded applications. In this study, it is first put forward that the key facial features of pigs can be extracted by PCA method first before the eigen face method is adopted for verification tests to reach an average accuracy rate of 74.4%; the key features, for which the most identifiable ones are in turn, respectively, face contour, nose, ears and other parts of pigs, can be visualized, and this is different from the identification features adopted in manual identification. This method not only reduces the computational complexity but also is of strong interpretability, so it is suitable for both the mobile terminals and the embedded applications. In some way, this study provides a systematic and stable guidance for livestock and poultry production.

Abstract in Chinese

生猪个体识别和行为分析是猪场智能管理的关键环节，以深度学习模型应用和改进为主计算机视觉技术已成为其主流，但模型运行对硬件要求高、可解释性不强，难以适应移动端和嵌入式应用，本研究提出首先采用PCA方法提取生猪脸部主要特征，并采用特征脸方法进行验证实验，取得74.4%的平均准确度，对其主要特征可视化，最具有辨识度的特征依次为生猪脸部轮廓、鼻子、耳朵和其他部分，与人工识别采用的辨识特征不一致，该方法减少了运算量，可解释性强，适合移动端和嵌入式应用，有利于对畜禽生产提供系统、稳定的指导。