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

Agriculture

Gaotian Liang¹  Peng Gao¹  Wenbin Chen¹  Mingxin Yang¹  Yufeng Chen¹  Weixing Wang¹  Jiaxing Xie¹  Ping Zhou¹  Xiongzhe Han² 

[1] College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China;Department of Biosystems Engineering, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Korea;

关键词: Internet of Things;    bidirectional LSTM;    soil moisture and soil electrical conductivity prediction;    MLNN;    wireless sensor network;    citrus;   

DOI  :  10.3390/agriculture11070635

来源: DOAJ

【 摘 要 】

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 (R²) 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.

【 授权许可】

Unknown