Yangyang Guo, Yongliang Qiao, S. Sukkarieh, L. Chai, Dongjian He
{"title":"基于视频数据的BiGRU-Attention - Based奶牛行为分类在精准畜牧业中的应用","authors":"Yangyang Guo, Yongliang Qiao, S. Sukkarieh, L. Chai, Dongjian He","doi":"10.13031/trans.14658","DOIUrl":null,"url":null,"abstract":"HighlightsBiGRU-attention based cow behavior classification was proposed.Key spatial-temporal features were captured for behavior representation.BiGRU-attention achieved >82% classification accuracy on calf and adult cow datasets.The proposed method could be used for similar animal behavior classification.Abstract. Animal behavior consists of time series activities, which can reflect animals’ health and welfare status. Monitoring and classifying animal behavior facilitates management decisions to optimize animal performance, welfare, and environmental outcomes. In recent years, deep learning methods have been applied to monitor animal behavior worldwide. To achieve high behavior classification accuracy, a BiGRU-attention based method is proposed in this article to classify some common behaviors, such as exploring, feeding, grooming, standing, and walking. In our work, (1) Inception-V3 was first applied to extract convolutional neural network (CNN) features for each image frame in videos, (2) bidirectional gated recurrent unit (BiGRU) was used to further extract spatial-temporal features, (3) an attention mechanism was deployed to allocate weights to each of the extracted spatial-temporal features according to feature similarity, and (4) the weighted spatial-temporal features were fed to a Softmax layer for behavior classification. Experiments were conducted on two datasets (i.e., calf and adult cow), and the proposed method achieved 82.35% and 82.26% classification accuracy on the calf and adult cow datasets, respectively. In addition, in comparison with other methods, the proposed BiGRU-attention method outperformed long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and BiGRU. Overall, the proposed BiGRU-attention method can capture key spatial-temporal features to significantly improve animal behavior classification, which is favorable for automatic behavior classification in precision livestock farming. Keywords: BiGRU, Cow behavior, Deep learning, LSTM, Precision livestock farming.","PeriodicalId":23120,"journal":{"name":"Transactions of the ASABE","volume":"178 1","pages":""},"PeriodicalIF":1.4000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"BiGRU-Attention Based Cow Behavior Classification Using Video Data for Precision Livestock Farming\",\"authors\":\"Yangyang Guo, Yongliang Qiao, S. Sukkarieh, L. Chai, Dongjian He\",\"doi\":\"10.13031/trans.14658\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"HighlightsBiGRU-attention based cow behavior classification was proposed.Key spatial-temporal features were captured for behavior representation.BiGRU-attention achieved >82% classification accuracy on calf and adult cow datasets.The proposed method could be used for similar animal behavior classification.Abstract. Animal behavior consists of time series activities, which can reflect animals’ health and welfare status. Monitoring and classifying animal behavior facilitates management decisions to optimize animal performance, welfare, and environmental outcomes. In recent years, deep learning methods have been applied to monitor animal behavior worldwide. To achieve high behavior classification accuracy, a BiGRU-attention based method is proposed in this article to classify some common behaviors, such as exploring, feeding, grooming, standing, and walking. In our work, (1) Inception-V3 was first applied to extract convolutional neural network (CNN) features for each image frame in videos, (2) bidirectional gated recurrent unit (BiGRU) was used to further extract spatial-temporal features, (3) an attention mechanism was deployed to allocate weights to each of the extracted spatial-temporal features according to feature similarity, and (4) the weighted spatial-temporal features were fed to a Softmax layer for behavior classification. Experiments were conducted on two datasets (i.e., calf and adult cow), and the proposed method achieved 82.35% and 82.26% classification accuracy on the calf and adult cow datasets, respectively. In addition, in comparison with other methods, the proposed BiGRU-attention method outperformed long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and BiGRU. Overall, the proposed BiGRU-attention method can capture key spatial-temporal features to significantly improve animal behavior classification, which is favorable for automatic behavior classification in precision livestock farming. Keywords: BiGRU, Cow behavior, Deep learning, LSTM, Precision livestock farming.\",\"PeriodicalId\":23120,\"journal\":{\"name\":\"Transactions of the ASABE\",\"volume\":\"178 1\",\"pages\":\"\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transactions of the ASABE\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.13031/trans.14658\",\"RegionNum\":4,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"AGRICULTURAL ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions of the ASABE","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.13031/trans.14658","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
BiGRU-Attention Based Cow Behavior Classification Using Video Data for Precision Livestock Farming
HighlightsBiGRU-attention based cow behavior classification was proposed.Key spatial-temporal features were captured for behavior representation.BiGRU-attention achieved >82% classification accuracy on calf and adult cow datasets.The proposed method could be used for similar animal behavior classification.Abstract. Animal behavior consists of time series activities, which can reflect animals’ health and welfare status. Monitoring and classifying animal behavior facilitates management decisions to optimize animal performance, welfare, and environmental outcomes. In recent years, deep learning methods have been applied to monitor animal behavior worldwide. To achieve high behavior classification accuracy, a BiGRU-attention based method is proposed in this article to classify some common behaviors, such as exploring, feeding, grooming, standing, and walking. In our work, (1) Inception-V3 was first applied to extract convolutional neural network (CNN) features for each image frame in videos, (2) bidirectional gated recurrent unit (BiGRU) was used to further extract spatial-temporal features, (3) an attention mechanism was deployed to allocate weights to each of the extracted spatial-temporal features according to feature similarity, and (4) the weighted spatial-temporal features were fed to a Softmax layer for behavior classification. Experiments were conducted on two datasets (i.e., calf and adult cow), and the proposed method achieved 82.35% and 82.26% classification accuracy on the calf and adult cow datasets, respectively. In addition, in comparison with other methods, the proposed BiGRU-attention method outperformed long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and BiGRU. Overall, the proposed BiGRU-attention method can capture key spatial-temporal features to significantly improve animal behavior classification, which is favorable for automatic behavior classification in precision livestock farming. Keywords: BiGRU, Cow behavior, Deep learning, LSTM, Precision livestock farming.
期刊介绍:
This peer-reviewed journal publishes research that advances the engineering of agricultural, food, and biological systems. Submissions must include original data, analysis or design, or synthesis of existing information; research information for the improvement of education, design, construction, or manufacturing practice; or significant and convincing evidence that confirms and strengthens the findings of others or that revises ideas or challenges accepted theory.