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Recognition of feeding behaviour of pigs and determination of feeding time of each pig by a video-based deep learning method

Softmax函数 色调 模式识别(心理学) 人工智能 卷积神经网络 试验装置 分割 色空间 数学 计算机科学 计算机视觉 图像(数学)
作者
Chen Chen,Weixing Zhu,Juan P. Steibel,Janice M. Siegford,Junjie Han,Tomás Norton
出处
期刊:Computers and Electronics in Agriculture [Elsevier BV]
卷期号:176: 105642-105642 被引量:40
标识
DOI:10.1016/j.compag.2020.105642
摘要

Monitoring the feeding behaviour of pigs and measuring their feeding time can help farmers to evaluate the pig health and welfare. The aim of this study was to develop a video-based deep learning algorithm to recognise feeding behaviour of nursery pigs and determine the feeding time of pigs on an individual level. In the experiment, two pens of pigs were video recorded for 3 days. In the video of feeding region of pen 1, 96,000 1 s feeding episodes and 96,000 1 s non-feeding episodes were generated. 70% of these data was randomly selected as training set and the remaining 30% as validation set. In the video of feeding region of pen 2, all the labelled 24,656 1 s feeding episodes and 61,744 1 s non-feeding episodes were used as test set. Firstly, the Convolutional Neural Network (CNN) architecture Xception was used to extract spatial features. These features were input into Long Short-term Memory (LSTM) framework to extract spatial-temporal features. Through the fully connected layer, the prediction function Softmax was finally used to classify these 1 s episodes as feeding or non-feeding. An image processing algorithm based on maximum entropy segmentation, HSV (Hue, Saturation and Value) colour space transformation and template matching was proposed to calculate the circularity of the head, the ratio of the head to the feeding sub-region, the accumulated pixels of the head motion, and the distance from the head to the number on pig back in order to determine the identity and feeding time of each pig. In the test set, the proposed algorithm could recognise feeding behaviour with an accuracy of 98.4%, a sensitivity of 98.8%, specificity of 98.3% and precision of 95.9%, and could correctly recognise 98.5% of feeding time of individual pigs from the total 45944 s feeding time of 8 pigs. The results indicate that the proposed method can be used to recognise feeding behavior of pigs and determine feeding time of each pig.
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