Performance evaluation of a water level sensor under various turbidity levels in lowland crop production systems

Autor: G S Pereira, R C Ramirez, E S Agulto, V B Ella
Rok vydání: 2022
Zdroj: IOP Conference Series: Earth and Environmental Science. 1038:012033
ISSN: 1755-1315
1755-1307
DOI: 10.1088/1755-1315/1038/1/012033
Popis: The practice of alternate wetting and drying (AWD), a water-saving technology in lowland crop production systems, can be greatly facilitated using wireless water level sensors. However, these sensors generally work under clear water conditions. The sensitivity of these sensors to turbidity is important for accurate water level measurement and appropriate irrigation scheduling. This study evaluated the performance of a high-end water level sensor of the submersible pressure transducer type under various turbidity levels. The performance tests were performed in the laboratory using water samples collected from a typical lowland rice production system under various levels of turbidity replicated three times with clear water as control. The readings of the sensors were compared with manual readings for each turbidity level in all replications. Results showed that the measured water level depth generally increases with increasing turbidity for each voltage level. The linear regression or calibration equation developed for each turbidity level proved to be adequate as evidenced by a relatively low RMSE of less than 1 mV. Results of ANOVA suggest that turbidity significantly affects the accuracy of the water level sensor (p ). A unified calibration equation (R2=0.9985 and RMSE=1.971 mV) was developed to account for the effect of turbidity up to 4300 FAU on the water level measurements. Results of this study can be used to improve the accuracy of water level monitoring in irrigated lowland crop production systems employing alternate wetting and drying technology to further increase irrigation efficiencies and augment water savings particularly during the dry season or under water-scarce conditions for a more sustainable crop production.
Databáze: OpenAIRE
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