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

Ruiqing Wang, Jinlei Feng, Wu Zhang (), Bo Liu, Tao Wang, Chenlu Zhang, Shaoxiang Xu, Lifu Zhang, Guanpeng Zuo, Yixi Lv, Zhe Zheng, Yu Hong and Xiuqi Wang
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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
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

Agriculture, 2023, vol. 13, issue 2, 1-20

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.

Keywords: tea plantation; deep learning; data feature extraction; data correction (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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