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

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-LST...

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Bibliographic Details
Published in:Agriculture (Basel) 2021-07, Vol.11 (7), p.635
Main Authors: Gao, Peng, Xie, Jiaxing, Yang, Mingxin, Zhou, Ping, Chen, Wenbin, Liang, Gaotian, Chen, Yufeng, Han, Xiongzhe, Wang, Weixing
Format: Article
Language:English
Subjects:
Air temperature
bidirectional LSTM
citrus
Citrus fruits
Data collection
Data integrity
Decision making
Electrical conductivity
Electrical resistivity
Environmental information
Fertilization
Fertilizers
Fruits
Internet of Things
Irrigation
Irrigation scheduling
Long short-term memory
MLNN
Multilayers
Neural networks
Orchards
Performance evaluation
Remote sensing
Root-mean-square errors
Scheduling
Sensors
Soil improvement
Soil moisture
soil moisture and soil electrical conductivity prediction
Vegetation
Water shortages
Wind speed
wireless sensor network
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Summary: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.
ISSN:2077-0472
2077-0472
DOI:10.3390/agriculture11070635