Gradient Descent with Momentum based Neural Network Pattern Classification for the Prediction of Soil Moisture Content in Precision Agriculture

2015 ◽  
Author(s):  
Saroj Kumar Lenka ◽  
Ambarish G. Mohapatra
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Moisture Content ◽  
Pattern Classification ◽  
Precision Agriculture ◽  
Gradient Descent ◽  
Soil Moisture Content ◽  
Network Pattern

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.

scholarly journals Precision Agriculture using IOT

2021 ◽  
Vol 9 (VII) ◽  
pp. 122-127
Author(s):  
Mr. V. Seetha Rama
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Precision Agriculture ◽  
Soil Moisture Content ◽  
Irrigation System ◽  
Control Unit ◽  
Moisture Level ◽  
Soil Moisture Sensors ◽  
The Status ◽  
Automated Irrigation System

Automation of farm activities can transform agricultural domain from being manual and static to intelligent and dynamic leading to higher production with lesser human supervision. This paper proposes an automated irrigation system which monitors and maintains the desired soil moisture content via automatic watering. Microcontroller ATMEGA328P on Arduino Uno platform is used to implement the control unit. The setup uses soil moisture sensors which measure the exact moisture level in soil. This value enables the system to use appropriate quantity of water which avoids over/under irrigation. IOT is used to keep the farmers updated about the status of sprinklers. Information from the sensors is regularly updated on a webpage using GSM-GPRS SIM900A modem through which a farmer can check whether the water sprinklers are ON/OFF at any given time. Also, the sensor readings are transmitted to a Thing speak channel to generate graphs for analysis.

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