Prediction and monitoring model for farmland environmental system using soil sensor and neural network algorithm

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F23%3APU150311" target="_blank" >RIV/00216305:26210/23:PU150311 - isvavai.cz</a>

Result on the web

<a href="https://www.degruyter.com/document/doi/10.1515/phys-2022-0224/html" target="_blank" >https://www.degruyter.com/document/doi/10.1515/phys-2022-0224/html</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1515/phys-2022-0224" target="_blank" >10.1515/phys-2022-0224</a>

Result language

angličtina

Original language name

Prediction and monitoring model for farmland environmental system using soil sensor and neural network algorithm

Original language description

In this study, data fusion algorithm is used to classify the soil species and calibrate the soil humidity sensor, and by using edge computing and a wireless sensor network, farmland environment monitoring system with a two-stage calibration function of frequency domain reflectometer (FDR) is established. Edge computing is used in system nodes, including the saturation value of the soil humidity sensor, the calculated soil hardness, the calculation process of the neural network, and the model of soil classification. A bagged tree is adopted to avoid over-fitting to reduce the prediction variance of the decision tree. A decision tree model is established on each training set, and the C4.5 algorithm is adopted to construct each decision tree. After primary calibration, the root mean squared error (RMSE) between the measured and standard values is reduced to less than 0.0849%. The mean squared error (MSE) and mean absolute error (MAE) are reduced to less than 0.7208 and 0.6929%. The bagged tree model and backpropagation neural network are used to classify the soil and train the dynamic soil dataset. The output of the trained neural network is closer to the actual soil humidity than that of the FDR soil humidity sensor. The MAE, the MSE, and the RMSE decrease by 1.37%, 3.79, and 1.86%. With accurate measurements of soil humidity, this research shows an important guiding significance for improving the utilization efficiency of agricultural water, saving agricultural water, and formulating the crop irrigation process.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10500 - Earth and related environmental sciences

Project

<a href="/en/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Sustainable Process Integration Laboratory (SPIL)</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Open Physics

ISSN

2391-5471

e-ISSN

—

Volume of the periodical

21

Issue of the periodical within the volume

1

Country of publishing house

PL - POLAND

Number of pages

17

Pages from-to

„“-„“

UT code for WoS article

000926250600001

EID of the result in the Scopus database

2-s2.0-85147688901