Monitoring pig drinking has been a topic of interest to pig researchers and producers for many years. However, challenges still remain due to the fact that pigs like to play with drinkers in nursery environments and that the drinking pig is often touching others. These factors negatively influence the performance of camera-based pig drinking detection algorithms. The aim of this study is to investigate a deep learning method based on convolutional neural network (CNN) and long short-term memory (LSTM) to classify drinking and drinker-playing. In the experiment, two pens of pigs were video recorded for 3 days. In video from the first pen, 5400 2 s drinking episodes and 5400 2 s drinker-playing episodes were generated with 80% of these data being allocated as training set and the remaining 20% as validation set. In video from the second pen, 12,000 2 s drinking and drinker-playing episodes were generated as a test set. Firstly, the CNN architecture ResNet50 was used to extract spatial features. These features were input into LSTM framework to extract spatial–temporal features. Through the fully connected layer, the prediction function Softmax was finally used to classify these drinking and drinker-playing episodes. In the test set, the classification accuracy in the body and head regions of interest was 87.2% and 92.5%, respectively. The results indicate that the proposed method can be used to classify pigs’ drinking and drinker-playing. These classification results have potential to improve the accuracy of pig drinking detection and help farmers to estimate pig welfare.

中文翻译：

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基于视频的深度学习方法对猪的饮酒和饮酒行为进行分类

多年来，监测猪饮水一直是猪研究人员和生产者感兴趣的话题。然而，由于猪喜欢在育苗环境中与饮水器玩耍并且饮水猪经常接触其他人，因此挑战仍然存在。这些因素会对基于摄像头的猪饮水检测算法的性能产生负面影响。本研究的目的是研究一种基于卷积神经网络 (CNN) 和长短期记忆 (LSTM) 的深度学习方法来对饮酒和饮酒行为进行分类。实验中，对两圈猪进行了为期3天的视频录制。在第一支笔的视频中，生成了 5400 2 s 饮酒情节和 5400 2 s 饮酒者玩耍情节，其中 80% 的数据被分配为训练集，其余 20% 作为验证集。在第二支笔的视频中，12，生成 000 2 s 饮酒和饮酒的情节作为测试集。首先，使用CNN架构ResNet50提取空间特征。这些特征被输入到 LSTM 框架中以提取时空特征。通过全连接层，最终使用预测函数Softmax对这些喝酒和喝酒的情节进行分类。在测试集中，身体和头部感兴趣区域的分类准确率分别为 87.2% 和 92.5%。结果表明，所提出的方法可用于对猪的饮酒和饮酒行为进行分类。这些分类结果有可能提高猪饮水检测的准确性，并帮助农民估计猪的福利。CNN 架构 ResNet50 用于提取空间特征。这些特征被输入到 LSTM 框架中以提取时空特征。通过全连接层，最终使用预测函数Softmax对这些喝酒和喝酒的情节进行分类。在测试集中，身体和头部感兴趣区域的分类准确率分别为 87.2% 和 92.5%。结果表明，所提出的方法可用于对猪的饮酒和饮酒行为进行分类。这些分类结果有可能提高猪饮水检测的准确性，并帮助农民估计猪的福利。CNN 架构 ResNet50 用于提取空间特征。这些特征被输入到 LSTM 框架中以提取时空特征。通过全连接层，最终使用预测函数Softmax对这些喝酒和喝酒的情节进行分类。在测试集中，身体和头部感兴趣区域的分类准确率分别为 87.2% 和 92.5%。结果表明，所提出的方法可用于对猪的饮酒和饮酒行为进行分类。这些分类结果有可能提高猪饮水检测的准确性，并帮助农民估计猪的福利。最终使用预测函数 Softmax 对这些饮酒和饮酒事件进行分类。在测试集中，身体和头部感兴趣区域的分类准确率分别为 87.2% 和 92.5%。结果表明，所提出的方法可用于对猪的饮酒和饮酒行为进行分类。这些分类结果有可能提高猪饮水检测的准确性，并帮助农民估计猪的福利。最终使用预测函数 Softmax 对这些饮酒和饮酒事件进行分类。在测试集中，身体和头部感兴趣区域的分类准确率分别为 87.2% 和 92.5%。结果表明，所提出的方法可用于对猪的饮酒和饮酒行为进行分类。这些分类结果有可能提高猪饮水检测的准确性，并帮助农民估计猪的福利。