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Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling
2018
Sensors
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This paper presents a Dynamic Neural Network approach for modelling of the temporal soil moisture fluxes. ...
The application of the Dynamic Neural Network models in a predictive irrigation scheduling system was demonstrated using AQUACROP simulations of the potato-growing season. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s18103408
pmid:30314346
pmcid:PMC6210977
fatcat:hrnag4lwjbf7lpvyw33l3ssvwi
Evaluating the Neural Network Ensemble Method in Predicting Soil Moisture in Agricultural Fields
2021
Agronomy
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In this study, a neural network ensemble (NNE) method was employed to predict the soil moisture to eliminate the effects of random initial parameters of neural network (NN) on model accuracy. ...
The result showed that with 100 randomly initialized NN models, the NNE model achieved an average R2 of 0.96 and nRMSE of 5.93%, suggesting that the NNE model learned the soil moisture dynamics well and ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/agronomy11081521
fatcat:xr2ytzoowjeyzl3irfd5hfgcmi
Precision Irrigation Management Using Machine Learning and Digital Farming Solutions
2022
AgriEngineering
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Freshwater is essential for irrigation and the supply of nutrients for plant growth, in order to compensate for the inadequacies of rainfall. ...
This article reviews the research trend and applicability of machine learning techniques, as well as the deployment of developed machine learning models for use by farmers toward sustainable irrigation ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/agriengineering4010006
fatcat:ld6yznzpkvcppn6zfxwpsfoyuq
Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model
2016
Journal of Arid Land
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the precise irrigation scheduling. ...
The widely used neural network, multi-layer perceptron (MLP), was utilized for comparison to DBN. ...
We greatly thank HiWATER for sharing the soil moisture datasets of BNUNET (Beijing Normal University Sensor Network), WATERNET and SoilNET. The authors are also grateful to Prof. ...
doi:10.1007/s40333-016-0049-0
fatcat:mdne53wnlzhdrkwfif6erhv46y
LSTM-based model predictive control with discrete inputs for irrigation scheduling
[article]
2021
arXiv
pre-print
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In this article, a model predictive control (MPC) with discrete actuators is developed for irrigation scheduling, where a long short-term memory (LSTM) model of the soil-water-atmosphere system is used ...
schedules that are typical of irrigation practice. ...
Acknowledgment Financial support from Alberta Innovates and Natural Sciences and Engineering Research Council of Canada is gratefully acknowledged. ...
arXiv:2112.06352v1
fatcat:6yuex6dc4vcfnig7vza2wxwz6i
Neural network models for soil moisture forecasting from remote sensed measurements
2020
ACTA IMEKO
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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 ...
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 ...
Brocca (Research Institute for Geo-Hydrological Protection IRPI, Italian National Research Council) for the useful discussion and the advice on the use of remotely sensed data. ...
doi:10.21014/acta_imeko.v9i2.797
fatcat:ay2ywkfsvbcgfmolaqyrhjmn7q
Hourly Real-Time Rainfall Estimation for Improved Smart Irrigation System Using Nearby Automated Weather Station
2016
British Journal of Applied Science and Technology
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Author NH designed the literature searches, study, performed the statistical analysis and wrote the first draft of the manuscript. Author KK managed the analyses of the study. ...
In soil moisture sensors based irrigation systems, one used by [8] , where soil moisture sensor measures volumetric water content in the soil. ...
the data is given in used for training neural network, where as rest of below Fig.8 and was able to predict correct data data are used for testing. ...
doi:10.9734/bjast/2016/30934
fatcat:f6bdhlceoze7rpkxazccfvtg6m
Review of conceptual and systematic progress of precision irrigation
2021
International Journal of Agricultural and Biological Engineering
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, soil water redistribution, and soil moisture uniformity so that the effectiveness quality of irrigation infiltration could be improved remarkably. ...
modelling, and effectiveness evaluation, indicating that advanced irrigation optimization methods support higher productivity of crop field and better environmental conditions of soil; Current schedule ...
The authors also want to thank the editors for their hard work and the referees for their kind comments and valuable suggestions to improve this paper. [References] ...
doi:10.25165/j.ijabe.20211404.5463
fatcat:heekk26vybbe5m3shmz7zar6yy
Improved Soil Moisture and Electrical Conductivity Prediction of Citrus Orchards Based on IoT Using Deep Bidirectional LSTM
2021
Agriculture
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The performance of the deep Bid-LSTM model was compared with a multi-layer neural network (MLNN). ...
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 ...
Acknowledgments: The authors would like to thank all authors for openly providing the source codes used in the experimental comparison in this work. ...
doi:10.3390/agriculture11070635
fatcat:ywrvjhzx75hvlbfy4bmntebgbq
An artificial neural network approach to the estimation of stem water potential from frequency domain reflectometry soil moisture measurements and meteorological data
2013
Computers and Electronics in Agriculture
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An artificial neural network approach to the estimation of stem water potential from frequency domain reflectometry soil moisture measurements and meteorological data. ...
However, 69 optimization of irrigation scheduling using soil moisture sensors . 81 As an alternative to soil water content monitoring, some studies 82 suggest that plant-based measurements offer ...
uptake of water and trends 60 in soil moisture content with time during the irrigation season 61 (Hanson et al., 2000) . ...
doi:10.1016/j.compag.2012.12.001
fatcat:7r6gd5hsxrh6hlumlvmryid5na
Performance Analysis of Regression-Machine Learning Algorithms for Predication of Runoff Time
2019
Agrotechnology
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We propose Runoff time model which accepts irrigation depth, soil moisture and crop stage and time of concentration as input parameters and estimate runoff time. ...
A comparison has been made among these algorithms to choose best algorithm for irrigation runoff time prediction. ...
In the similar context Mohapatra and Kumar [11] proposed neural network pattern classification technique for the forecast of soil Moisture Content (MC) by considering soil and environmental parameters ...
doi:10.35248/2168-9881.19.8.187
fatcat:qwnekndj7fbgteg4l3fk7w75iu
Modeling for the Prediction of Soil Moisture in Litchi Orchard with Deep Long Short-Term Memory
2021
Agriculture
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With the increasing demand for agricultural irrigation water resources, evaluating soil moisture in advance to create a reasonable irrigation schedule would help improve water resource utilization. ...
The Deep-LSTM model has five layers with the fused time series data to predict the soil moisture of a litchi orchard in four different growth seasons. ...
We are thankful to the Conghua litchi orchard of Guangzhou, China.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/agriculture12010025
fatcat:rsujazvh3vgdpkwsj2iezftfqy
Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation
2017
Sustainability
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The review also shows that adaptive decision support systems based on model predictive control are able to adequately account for the time-varying nature of the soil-plant-atmosphere system while considering ...
Nevertheless, future research is needed for identifying crop response to regulated water deficits, developing improved soil moisture and plant sensors, and developing self-learning crop simulation frameworks ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/su9030353
fatcat:i4i34fqu7jadviukfstrxdtk5e
Water Allocation and Integrative Management of Precision Irrigation: A Systematic Review
2020
Water
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, integrative, and evolutionary irrigation system while providing the higher quality and efficiency needed for a full application of precision irrigation. ...
management contribute to the high-efficiency performance of precision irrigation techniques; the irrigation models, irrigation infrastructure, and management strategies currently being used are emphasized ...
Acknowledgments: We thank the editors for their hard work and the referees for their comments and valuable suggestions that helped to improve this paper. ...
doi:10.3390/w12113135
fatcat:q3ftrtlafzf2bikxrh2a5jnfce
Low Cost Design of Automated Drip Irrigation System with GSM
2019
International journal of recent technology and engineering
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The soil moisture and amount of flow of water in each sector are major consideration to design a fail-safe system for a variety of crops planted at a time. ...
This paper is about an automatic irrigation control system which is cost effective and can be used for irrigation by a farmer. ...
This paper has presented a dynamic neural network approach for modelling the time series of soil moisture content. ...
doi:10.35940/ijrte.d8357.118419
fatcat:x2gehrf6rvco5ent4npiqkpusy
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