TY - GEN
T1 - Calibration Repeatability of Soil Moisture Sensors in Biochar Amendments under Changing Salinity
AU - Gómez-Astorga, María José
AU - Villagra-Mendoza, Karolina
AU - Masís-Meléndez, Federico
AU - Rimolo-Donadio, Renato
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Soil moisture sensors play a key role in the efficient management of irrigation systems, contributing to the optimization of water. They often require initial calibration, and their accuracy over time is a valid concern, considering sensor drift and aging, in particular for low-cost sensors. This paper evaluates two types of soil moisture sensors - reflective and capacitive - after different calibrations at different times, using soil amended with biochar and different salinity levels. Sensor readings were compared against a direct measurement method, and accuracy and repeatability were assessed using three statistical criteria. Results show that the calibration procedure needs to consider the material composition of the soil to ensure reliable performance. Reflective sensors demonstrated better stability, maintaining errors below 10% while capacitive sensors showed errors approaching 30%, as biochar and salinity increased.
AB - Soil moisture sensors play a key role in the efficient management of irrigation systems, contributing to the optimization of water. They often require initial calibration, and their accuracy over time is a valid concern, considering sensor drift and aging, in particular for low-cost sensors. This paper evaluates two types of soil moisture sensors - reflective and capacitive - after different calibrations at different times, using soil amended with biochar and different salinity levels. Sensor readings were compared against a direct measurement method, and accuracy and repeatability were assessed using three statistical criteria. Results show that the calibration procedure needs to consider the material composition of the soil to ensure reliable performance. Reflective sensors demonstrated better stability, maintaining errors below 10% while capacitive sensors showed errors approaching 30%, as biochar and salinity increased.
KW - Internet of Things
KW - capacitance
KW - irrigation
KW - precision agriculture
KW - reflectometer
KW - soil analysis
UR - https://www.scopus.com/pages/publications/105042263529
U2 - 10.1109/CAFE66884.2025.11547608
DO - 10.1109/CAFE66884.2025.11547608
M3 - Contribución a la conferencia
AN - SCOPUS:105042263529
T3 - 2025 IEEE Conference on AgriFood Electronics, CAFE 2025
BT - 2025 IEEE Conference on AgriFood Electronics, CAFE 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE Conference on AgriFood Electronics, CAFE 2025
Y2 - 8 October 2025 through 10 October 2025
ER -