A real-time prediction system of soil moisture content using genetic neural network based on annealing algorithm

2008 ◽  
Author(s):  
Ruiyu Liang ◽  
Yanqiong Ding ◽  
Xuewu Zhang ◽  
Wenchao Zhang
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Moisture Content ◽  
Real Time ◽  
Soil Moisture Content ◽  
Prediction System ◽  
Time Prediction ◽  
Genetic Neural Network ◽  
Annealing Algorithm

scholarly journals Development and testing of a capacitive digital soil moisture sensor with printed circuit board as a probe

2020 ◽  
Vol 39 (3) ◽  
pp. 911-917
Author(s):  
V. Ogwo ◽  
K.N. Ogbu ◽  
C.C. Anyadike ◽  
O.A. Nwoke ◽  
C.C. Mbajiorgu
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Real Time ◽  
Printed Circuit Board ◽  
Soil Moisture Content ◽  
Gravimetric Method ◽  
Circuit Board ◽  
Moisture Sensor ◽  
Printed Circuit ◽  
Soil Moisture Sensor

The quantity and quality of water present in the soil determine to a greater extent the performance of agricultural crops. Real-time determination of moisture content has a greater advantage over the traditional gravimetric method of determining soil moisture content. Thus, this work was based on the design and construction of a cost effective digital capacitive soil moisture sensor for real-time measurement. The moisture sensors comprised four integrated units namely: power supply unit with a 9V DC battery as a power source, sensor unit with a locally sourced Printed Circuit Board (PCB) as the single sensing probe, control unit made up of PIC16f877 microcontroller programmed with a C language and the C source code compiled in Corporate Computer Services Compiler (CSS C) compiler development environment, and a 16x2 display unit which displays the readings in percentage moisture content (%MC) and capacitance (μF) of the soil obtained from the sensor on its screen. Standard gravimetric moisture content was carried out to get the calibration factor which was used to calibrate the sensor for reliability. The validation was done by taking the reprogrammed (calibrated) sensor to the field for further measurement, after which soil samples were collected for further gravimetric analysis. A regression equation was obtained by plotting the moisture content obtained from gravimetric method (%MCG) against that from sensor reading (%MCS) with a high degree correlation coefficient (R2) of 0.998. The developed capacitive soil moisture sensor is cheap, portable, reliable and easy to use even by local farmers. Keywords: Calibration, Capacitive sensor, Printed circuit board, Soil moisture content, Validation.

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.

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 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 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 Can Neural Networks Forecast Open Field Burning of Crop Residue in Regions with Anthropogenic Management and Control? A Case Study in Northeastern China

2021 ◽  
Vol 13 (19) ◽  
pp. 3988
Author(s):  
Bing Bai ◽  
Hongmei Zhao ◽  
Sumei Zhang ◽  
Xuelei Zhang ◽  
Yabin Du
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Moisture Content ◽  
Crop Residue ◽  
Soil Moisture Content ◽  
Northeastern China ◽  
Forecasting Accuracy ◽  
Open Burning ◽  
Natural Factors ◽  
And Control

Open burning is often used to remove crop residue during the harvest season. Despite a series of regulations by the Chinese government, the open burning of crop residue still frequently occurs in China, and the monitoring and forecasting crop fires have become a topic of active research. In this paper, crop fires in Northeastern China were forecasted using an artificial neural network (ANN) based on moderate-resolution imaging spectroradiometer (MODIS) satellite fire data from 2013–2020. Both natural factors (meteorological, soil moisture content, harvest date) and anthropogenic factors were considered. The model’s forecasting accuracy under natural factors reached 77.01% during 2013–2017. When considering the influence of anthropogenic management and control policies, such as the straw open burning prohibition areas in Jilin Province, the accuracy of the forecast results for 2020 was reduced to 60%. Although the forecasting accuracy was lower than for natural factors, the relative error between the observed fire points and the back propagation neural network (BPNN) forecasting results was acceptable. In terms of influencing factors, air pressure, the change in soil moisture content in a 24h period and the daily soil moisture content were significantly correlated with open burning. The results of this study improve our ability to forecast agricultural fires and provide a scientific framework for regional prevention and control of crop residue burning.

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