Diagnosis of dairy cow diseases by knowledge-driven deep learning based on the text reports of illness state
•Text reports of illness state are used for rapid diagnosis of dairy cow disease.•External knowledge graph is employed to extract more implicit information.•The hybrid Bi-LSTM-CNN framework captures global long-terms and local features.•Knowledge-driven deep learning makes effective diagnosis. Exper...
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| Published in | Computers and electronics in agriculture Vol. 205; p. 107564 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
01.02.2023
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| Abstract | •Text reports of illness state are used for rapid diagnosis of dairy cow disease.•External knowledge graph is employed to extract more implicit information.•The hybrid Bi-LSTM-CNN framework captures global long-terms and local features.•Knowledge-driven deep learning makes effective diagnosis.
Expert system is the most commonly used method for auxiliary diagnosis of dairy cow diseases, which is complex to build and usually difficult for non-professional farmers to operate. Moreover, it cannot discover the implicit knowledge hidden in the observed symptoms. To address these problems, we proposed a knowledge-driven deep learning model for efficient diagnosis of dairy cow diseases. The model first selected the explicit features from the text reports of illness state. Then, our model employed a professional knowledge graph of dairy cow diseases for extracting implicit features. Both the explicit and implicit features were furtherly fed into a BiLSTM-CNN hybrid network to make a diagnosis. The experimental results showed that the F1 value of our model reached 94.89%, which was 9.53% and 2.49% higher than that of the best machine learning model XGBoost and the neural network model DE-CNN, respectively. Our model can accurately diagnose dairy cow diseases, especially those with similar or common symptoms, and it will provide a new idea for the auxiliary disease diagnosis of other animals. |
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| AbstractList | •Text reports of illness state are used for rapid diagnosis of dairy cow disease.•External knowledge graph is employed to extract more implicit information.•The hybrid Bi-LSTM-CNN framework captures global long-terms and local features.•Knowledge-driven deep learning makes effective diagnosis.
Expert system is the most commonly used method for auxiliary diagnosis of dairy cow diseases, which is complex to build and usually difficult for non-professional farmers to operate. Moreover, it cannot discover the implicit knowledge hidden in the observed symptoms. To address these problems, we proposed a knowledge-driven deep learning model for efficient diagnosis of dairy cow diseases. The model first selected the explicit features from the text reports of illness state. Then, our model employed a professional knowledge graph of dairy cow diseases for extracting implicit features. Both the explicit and implicit features were furtherly fed into a BiLSTM-CNN hybrid network to make a diagnosis. The experimental results showed that the F1 value of our model reached 94.89%, which was 9.53% and 2.49% higher than that of the best machine learning model XGBoost and the neural network model DE-CNN, respectively. Our model can accurately diagnose dairy cow diseases, especially those with similar or common symptoms, and it will provide a new idea for the auxiliary disease diagnosis of other animals. |
| ArticleNumber | 107564 |
| Author | Wang, Haodong A, Xiaohui Qiu, Bailong Zhang, Qinggang Gao, Meng Zhang, Yi Shen, Weizheng Du, Haitao |
| Author_xml | – sequence: 1 givenname: Haodong surname: Wang fullname: Wang, Haodong organization: College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China – sequence: 2 givenname: Weizheng surname: Shen fullname: Shen, Weizheng email: wzshen@neau.edu.cn organization: College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China – sequence: 3 givenname: Yi surname: Zhang fullname: Zhang, Yi organization: College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China – sequence: 4 givenname: Meng surname: Gao fullname: Gao, Meng email: gaomeng@neau.edu.cn organization: College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China – sequence: 5 givenname: Qinggang surname: Zhang fullname: Zhang, Qinggang organization: Heilongjiang Animal Husbandry Station, Harbin 150030, China – sequence: 6 givenname: Xiaohui surname: A fullname: A, Xiaohui organization: Husbandry and Veterinary Branch of Heilongjiang Province Agricultural Academy, Harbin 150030, China – sequence: 7 givenname: Haitao surname: Du fullname: Du, Haitao organization: Dairy Association of Heilongjiang Province, Harbin 150030, China – sequence: 8 givenname: Bailong surname: Qiu fullname: Qiu, Bailong organization: Institute of Mathematics and Computer Science, Northwest Minzu University, Lanzhou 730000, China |
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| Cites_doi | 10.1109/CompComm.2018.8780869 10.4018/IJHISI.2018010104 10.1016/j.compeleceng.2021.107423 10.1109/TKDE.2008.76 10.1016/j.procs.2015.08.355 10.1016/j.knosys.2021.107052 10.1137/1.9781611974348.49 10.1016/j.artmed.2019.101772 10.1016/j.imu.2020.100483 10.1016/j.csl.2020.101182 10.1016/j.jvcir.2020.102901 10.1016/j.procs.2017.10.005 10.1007/s12652-018-1095-6 10.1109/IIAI-AAI50415.2020.00179 10.1109/ICSC.2020.00060 10.1109/ACCESS.2022.3140342 10.18653/v1/2021.emnlp-demo.6 10.1142/S0218194021400039 |
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| Keywords | Deep learning Auxiliary disease diagnosis Dairy farming Text classification Knowledge graph |
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| SubjectTerms | Auxiliary disease diagnosis Dairy farming Deep learning Knowledge graph Text classification |
| Title | Diagnosis of dairy cow diseases by knowledge-driven deep learning based on the text reports of illness state |
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