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

Author:

Wang Ruiqing1ORCID,Feng Jinlei1,Zhang Wu12,Liu Bo1,Wang Tao1,Zhang Chenlu1,Xu Shaoxiang1,Zhang Lifu1,Zuo Guanpeng1,Lv Yixi1,Zheng Zhe1,Hong Yu1,Wang Xiuqi1

Affiliation:

1. School of Information and Computer, Anhui Agriculture University, Hefei 230036, China

2. Anhui Province Key Laboratory of Smart Agricultural Technology and Equipment, 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.

Funder

Key Research and Development Project of Anhui Province

Independent Project of Anhui Key Laboratory of Smart Agricultural Technology and Equipment

Publisher

MDPI AG

Subject

Plant Science,Agronomy and Crop Science,Food Science

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