Improved Soil Moisture and Electrical Conductivity Prediction of Citrus Orchards Based on IoT Using Deep Bidirectional LSTM

Peng Gao, Jiaxing Xie, Mingxin Yang, Ping Zhou, Wenbin Chen, Gaotian Liang, Yufeng Chen, Xiongzhe Han and Weixing Wang
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Peng Gao: College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China
Jiaxing Xie: College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China
Mingxin Yang: College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China
Ping Zhou: College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China
Wenbin Chen: College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China
Gaotian Liang: College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China
Yufeng Chen: College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China
Xiongzhe Han: Department of Biosystems Engineering, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Korea
Weixing Wang: College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2021, vol. 11, issue 7, 1-22

Abstract: In order to create an irrigation scheduling plan for use in large-area citrus orchards, an environmental information collection system of citrus orchards was established based on the Internet of Things (IoT). With the environmental information data, deep bidirectional long short-term memory (Bid-LSTM) networks are proposed to improve soil moisture (SM) and soil electrical conductivity (SEC) predictions, providing a meaningful reference for the irrigation and fertilization of citrus orchards. The IoT system contains SM, SEC, air temperature and humidity, wind speed, and precipitation sensors, while the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination ( R 2 ) were calculated to evaluate the performance of the models. The performance of the deep Bid-LSTM model was compared with a multi-layer neural network (MLNN). The results for the performance criteria reveal that the proposed deep Bid-LSTM networks perform better than the MLNN model, according to many of the evaluation indicators of this study.

Keywords: Internet of Things; bidirectional LSTM; soil moisture and soil electrical conductivity prediction; MLNN; wireless sensor network; citrus (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: 2021
References: View references in EconPapers View complete reference list from CitEc
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