<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper firstly constructs a PLC-based intelligent irrigation control system, analyzes the requirements of its system functions, and determines three major modules of the system: data acquisition module, intelligent irrigation module, and Web service module. Then the system fuzzy control process is determined. According to the moisture value collected by the soil moisture sensor, the average is taken, and the deviation and deviation change rate is obtained by calculating the difference with the set value, and the control value is obtained by querying the previously deposited fuzzy control table through a series of transformations, and the command is executed according to the program setting. The automatic irrigation requirement is achieved by controlling the solenoid valve on and off. Finally, the analysis of system functions in terms of sensor accuracy, system function testing, and temperature and air humidity prediction accuracy was carried out. Regarding sensor accuracy, the error of relative soil moisture content is within 3%, and the relative error of soil temperature is within 0.5. In the system control function test, the solenoid valve will open for irrigation when the soil relative water content is lower than 40% or higher than 70% of the warning range, and the lighting will be turned on when the light intensity is lower than 200 Lux, and the system control is in line with the expectation. The intelligent irrigation system is of great significance to agricultural development.</p>
</abstract>

This paper firstly constructs a PLC-based intelligent irrigation control system, analyzes the requirements of its system functions, and determines three major modules of the system: data acquisition module, intelligent irrigation module, and Web service module. Then the system fuzzy control process is determined. According to the moisture value collected by the soil moisture sensor, the average is taken, and the deviation and deviation change rate is obtained by calculating the difference with the set value, and the control value is obtained by querying the previously deposited fuzzy control table through a series of transformations, and the command is executed according to the program setting. The automatic irrigation requirement is achieved by controlling the solenoid valve on and off. Finally, the analysis of system functions in terms of sensor accuracy, system function testing, and temperature and air humidity prediction accuracy was carried out. Regarding sensor accuracy, the error of relative soil moisture content is within 3%, and the relative error of soil temperature is within 0.5. In the system control function test, the solenoid valve will open for irrigation when the soil relative water content is lower than 40% or higher than 70% of the warning range, and the lighting will be turned on when the light intensity is lower than 200 Lux, and the system control is in line with the expectation. The intelligent irrigation system is of great significance to agricultural development.

Construction of an intelligent agricultural irrigation control platform based on PLC technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper firstly constructs a PLC-based intelligent irrigation control system, analyzes the requirements of its system functions, and determines three major...

Construction of an intelligent agricultural irrigation control platform based on PLC technology

Publié en ligne: 18 sept. 2023

Pages: -

Reçu: 10 nov. 2022

Accepté: 29 mars 2023

DOI: https://doi.org/10.2478/amns.2023.2.00344

Mots clés
PLC-control, Fuzzy control, Intelligent agriculture, Automatic irrigation, Data visualization

© 2023 Miaoqi Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Construction of an intelligent agricultural irrigation control platform based on PLC technology

Publié en ligne: 18 sept. 2023

Pages: -

Reçu: 10 nov. 2022

Accepté: 29 mars 2023

DOI: https://doi.org/10.2478/amns.2023.2.00344

Mots clésPLC-control, Fuzzy control, Intelligent agriculture, Automatic irrigation, Data visualization

© 2023 Miaoqi Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
PLC-control, Fuzzy control, Intelligent agriculture, Automatic irrigation, Data visualization