scholarly journals Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3408 ◽  
Author(s):  
Olutobi Adeyemi ◽  
Ivan Grove ◽  
Sven Peets ◽  
Yuvraj Domun ◽  
Tomas Norton
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Irrigation Scheduling ◽  
Neural Network Models ◽  
Dynamic Neural Network ◽  
Rule Based ◽  
Scheduling System ◽  
Rule Based System

Sustainable freshwater management is underpinned by technologies which improve the efficiency of agricultural irrigation systems. Irrigation scheduling has the potential to incorporate real-time feedback from soil moisture and climatic sensors. However, for robust closed-loop decision support, models of the soil moisture dynamics are essential in order to predict crop water needs while adapting to external perturbation and disturbances. This paper presents a Dynamic Neural Network approach for modelling of the temporal soil moisture fluxes. The models are trained to generate a one-day-ahead prediction of the volumetric soil moisture content based on past soil moisture, precipitation, and climatic measurements. Using field data from three sites, a R 2 value above 0.94 was obtained during model evaluation in all sites. The models were also able to generate robust soil moisture predictions for independent sites which were not used in training the models. The application of the Dynamic Neural Network models in a predictive irrigation scheduling system was demonstrated using AQUACROP simulations of the potato-growing season. The predictive irrigation scheduling system was evaluated against a rule-based system that applies irrigation based on predefined thresholds. Results indicate that the predictive system achieves a water saving ranging between 20 and 46% while realizing a yield and water use efficiency similar to that of the rule-based system.

scholarly journals Development and integration of soil moisture sensor with drip system for precise irrigation scheduling through mobile phone

2016 ◽  
Vol 8 (4) ◽  
pp. 1959-1965 ◽  
Author(s):  
Jitendra Kumar ◽  
Neelam Patel ◽  
T. B. S. Rajput
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Mobile Phone ◽  
Real Time ◽  
Soil Moisture Content ◽  
Irrigation System ◽  
Irrigation Scheduling ◽  
Moisture Sensor ◽  
Soil Moisture Sensor ◽  
Level Sensor

Soil moisture sensor is an instrument for quick measurements of soil moisture content in the crop root zone on real time basis. The main objective of this research was development and evaluation of an indigenous sensor for precise irrigation scheduling. The various parts of sensor developed were ceramic cup, acrylic pipe, level sensor, tee, reducer, gland, cork, and end cap. The designed system was successfully tested on okra crop and calibrated with frequency domain reflectometry (FDR) by three methods of irrigation, i.e. check basin, furrow and drip, respectively. The average depth of water depletion in modified tensiometer by these methods was 27 to 35 cm at 50% management allowable depletion (MAD) of field capacity. This depth was useful for the level sensor to be installed inside modified tensiometer for real time irrigation scheduling. The correlation coefficient (R2) between soil moisture content obtained from the developed sensor and FDR was 0.963. Sensor network was integrated with global system for mobile communication (GSM), short message service (SMS) and drip head work to develop an automated irrigation system. This would enable farmers to effectively monitor and control water application in the field by sending command through SMS and receiving pumping status through the mobile phone.

scholarly journals Sensitivity of PR2 Capacitance Soil Moisture Meter for Irrigation Scheduling

2021 ◽  
Vol 1 (3) ◽  
pp. 1-5
Author(s):  
Olotu Y.* ◽  
◽  
Omoakhalen A.I. ◽  
Ososomi A.S. ◽  
Gbalaja Mayowa ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Organic Soil ◽  
Integrated Water Resources Management ◽  
Irrigation Scheduling ◽  
Computer Algorithms ◽  
Resources Management ◽  
Moisture Content Measurement ◽  
Complex Computer

Accurate soil moisture content measurement is essential for designing a robust irrigation scheduling and integrated water resources management (I.W.R.M.). Capacitance-based sensors have widely been used to monitor soil moisture at different measuring depths coupled with continuous and instantaneous outputs. This study's objective was to evaluate the PR2 capacitance moisture meter's performance on mineral and organic soil water content. The outputs of PR2 in m3 /m3 and vol.% were compared with gravimetrically measured soil moisture. The R.M.S.E. measurement at Site A at the first and second replicates increased from 0.49% to 0.67%. In contrast, the r2 value of 0.99 was obtained for the two replications when comparing the soil moisture content observed from gravimetric measurement and the automated outputs from the PR2 Probe soil monitor. The R.M.S.E. values were 0.48%, and 1.32% were estimated for the first and second replications at Site B. The result indicates that the PR2 Profile Probe could be a reliable alternative to other time-consuming, complex computer algorithms for accurate point measurement of soil moisture.

Prediction of Soil Moisture Content Based on Improved BP Neural Network

2021 ◽  
Author(s):  
He Shulin ◽  
Liu Yong ◽  
Sun Haiyang ◽  
Zheng Kaiwen ◽  
Zhang Yandi
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Moisture Content ◽  
Bp Neural Network ◽  
Soil Moisture Content

scholarly journals Soil Moisture Retrieval in Farmland Areas with Sentinel Multi-Source Data Based on Regression Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 877
Author(s):  
Jian Liu ◽  
Youshuan Xu ◽  
Henghui Li ◽  
Jiao Guo
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Remote Sensing Data ◽  
Optimal Combination ◽  
Optical Data ◽  
Support Vector ◽  
Sensing Data

As an important component of the earth ecosystem, soil moisture monitoring is of great significance in the fields of crop growth monitoring, crop yield estimation, variable irrigation, and other related applications. In order to mitigate or eliminate the impacts of sparse vegetation covers in farmland areas, this study combines multi-source remote sensing data from Sentinel-1 radar and Sentinel-2 optical satellites to quantitatively retrieve soil moisture content. Firstly, a traditional Oh model was applied to estimate soil moisture content after removing vegetation influence by a water cloud model. Secondly, support vector regression (SVR) and generalized regression neural network (GRNN) models were used to establish the relationships between various remote sensing features and real soil moisture. Finally, a regression convolutional neural network (CNNR) model is constructed to extract deep-level features of remote sensing data to increase soil moisture retrieval accuracy. In addition, polarimetric decomposition features for real Sentinel-1 PolSAR data are also included in the construction of inversion models. Based on the established soil moisture retrieval models, this study analyzes the influence of each input feature on the inversion accuracy in detail. The experimental results show that the optimal combination of R2 and root mean square error (RMSE) for SVR is 0.7619 and 0.0257 cm3/cm3, respectively. The optimal combination of R2 and RMSE for GRNN is 0.7098 and 0.0264 cm3/cm3, respectively. Especially, the CNNR model with optimal feature combination can generate inversion results with the highest accuracy, whose R2 and RMSE reach up to 0.8947 and 0.0208 cm3/cm3, respectively. Compared to other methods, the proposed algorithm improves the accuracy of soil moisture retrieval from synthetic aperture radar (SAR) and optical data. Furthermore, after adding polarization decomposition features, the R2 of CNNR is raised by 0.1524 and the RMSE of CNNR decreased by 0.0019 cm3/cm3 on average, which means that the addition of polarimetric decomposition features effectively improves the accuracy of soil moisture retrieval results.

scholarly journals Neural network models for soil moisture forecasting from remote sensed measurements

ACTA IMEKO ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 59
Author(s):  
Andrea Marini ◽  
Loris Francesco Termite ◽  
Alberto Garinei ◽  
Marcello Marconi ◽  
Lorenzo Biondi
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Short Term Memory ◽  
Meteorological Data ◽  
Network Models ◽  
Irrigation Scheduling ◽  
Machine Learning Techniques ◽  
Free Access ◽  
Neural Network Models ◽  
Inference System

Machine learning techniques are employed to describe the temporal behavior of soil moisture using meteorological data as inputs. Three different Artificial Neural Network models, a feedforward Multi-Layer Perceptron, a Long-Short Term Memory and the Adaptive Network-based Fuzzy Inference System, are trained and their results are compared. The soil moisture is expressed in terms of Soil Water Index, derived from satellite retrievals, with the last known value also being used as input. The results are promising as the proposed methodology relies on free-access data with a worldwide coverage, allowing to easily estimate the forthcoming soil moisture. The knowledge of the expected value of this variable could be extremely useful for irrigation scheduling and it is the basis of Decision Support Systems to efficiently manage water resources in agriculture.

scholarly journals Sensitivity of PR2 Capacitance Soil Moisture Meter for Irrigation Scheduling

2021 ◽  
pp. 1-5
Author(s):  
Olotu Y. ◽  
◽  
Omoakhalen A.I. ◽  
Ososomi A.S. ◽  
Gbalaja Mayowa ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Organic Soil ◽  
Integrated Water Resources Management ◽  
Irrigation Scheduling ◽  
Computer Algorithms ◽  
Resources Management ◽  
Moisture Content Measurement ◽  
Complex Computer

Accurate soil moisture content measurement is essential for designing a robust irrigation scheduling and integrated water resources management (I.W.R.M.). Capacitance-based sensors have widely been used to monitor soil moisture at different measuring depths coupled with continuous and instantaneous outputs. This study’s objective was to evaluate the PR2 capacitance moisture meter’s performance on mineral and organic soil water content. The outputs of PR2 in m3/m3 and vol.% were compared with gravimetrically measured soil moisture. The R.M.S.E. measurement at Site A at the first and second replicates increased from 0.49% to 0.67%. In contrast, the r2 value of 0.99 was obtained for the two replications when comparing the soil moisture content observed from gravimetric measurement and the automated outputs from the PR2 Probe soil monitor. The R.M.S.E. values were 0.48%, and 1.32% were estimated for the first and second replications at Site B. The result indicates that the PR2 Profile Probe could be a reliable alternative to other time-consuming, complex computer algorithms for accurate point measurement of soil moisture.

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