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Detection and Correction of Abnormal IoT Data from Tea Plantations Based on Deep Learning

Author

Listed:
  • Ruiqing Wang

    (School of Information and Computer, Anhui Agriculture University, Hefei 230036, China)

  • Jinlei Feng

    (School of Information and Computer, Anhui Agriculture University, Hefei 230036, China)

  • Wu Zhang

    (School of Information and Computer, Anhui Agriculture University, Hefei 230036, China
    Anhui Province Key Laboratory of Smart Agricultural Technology and Equipment, Anhui Agriculture University, Hefei 230036, China)

  • Bo Liu

    (School of Information and Computer, Anhui Agriculture University, Hefei 230036, China)

  • Tao Wang

    (School of Information and Computer, Anhui Agriculture University, Hefei 230036, China)

  • Chenlu Zhang

    (School of Information and Computer, Anhui Agriculture University, Hefei 230036, China)

  • Shaoxiang Xu

    (School of Information and Computer, Anhui Agriculture University, Hefei 230036, China)

  • Lifu Zhang

    (School of Information and Computer, Anhui Agriculture University, Hefei 230036, China)

  • Guanpeng Zuo

    (School of Information and Computer, Anhui Agriculture University, Hefei 230036, China)

  • Yixi Lv

    (School of Information and Computer, Anhui Agriculture University, Hefei 230036, China)

  • Zhe Zheng

    (School of Information and Computer, Anhui Agriculture University, Hefei 230036, China)

  • Yu Hong

    (School of Information and Computer, Anhui Agriculture University, Hefei 230036, China)

  • Xiuqi Wang

    (School of Information and Computer, Anhui Agriculture University, Hefei 230036, China)

Abstract

This paper proposes a data anomaly detection and correction algorithm for the tea plantation IoT system based on deep learning, aiming at the multi-cause and multi-feature characteristics of abnormal data. The algorithm is based on the Z-score standardization of the original data and the determination of sliding window size according to the sampling frequency. First, we construct a convolutional neural network (CNN) model to extract abnormal data. Second, based on the support vector machine (SVM) algorithm, the Gaussian radial basis function (RBF) and one-to-one (OVO) multiclassification method are used to classify the abnormal data. Then, after extracting the time points of abnormal data, a long short-term memory network is established for prediction with multifactor historical data. The predicted values are used to replace and correct the abnormal data. When multiple consecutive abnormal values are detected, a faulty sensor judgment is given, and the specific faulty sensor location is output. The results show that the accuracy rate and micro-specificity of abnormal data detection for the CNN-SVM model are 3–4% and 20–30% higher than those of the traditional CNN model, respectively. The anomaly detection and correction algorithm for tea plantation data established in this paper provides accurate performance.

Suggested Citation

  • Ruiqing Wang & Jinlei Feng & Wu Zhang & Bo Liu & Tao Wang & Chenlu Zhang & Shaoxiang Xu & Lifu Zhang & Guanpeng Zuo & Yixi Lv & Zhe Zheng & Yu Hong & Xiuqi Wang, 2023. "Detection and Correction of Abnormal IoT Data from Tea Plantations Based on Deep Learning," Agriculture, MDPI, vol. 13(2), pages 1-20, February.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:2:p:480-:d:1071924
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    References listed on IDEAS

    as
    1. Xue-Bo Jin & Xing-Hong Yu & Xiao-Yi Wang & Yu-Ting Bai & Ting-Li Su & Jian-Lei Kong, 2020. "Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System," Sustainability, MDPI, vol. 12(4), pages 1-18, February.
    2. Xue-Feng Wang & Xiao-Ming Sun & Yang Fang, 2008. "Genetic Algorithm Solution For Multi-Period Two-Echelon Integrated Competitive/Uncompetitive Facility Location Problem," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 25(01), pages 33-56.
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    Cited by:

    1. Widad Elouataoui & Saida El Mendili & Youssef Gahi, 2023. "An Automated Big Data Quality Anomaly Correction Framework Using Predictive Analysis," Data, MDPI, vol. 8(12), pages 1-22, December.

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