Santos et al.
Nativa, Sinop, v. 9, n. 1, p. 123-128, jan./fev. 2021.
127
For table 2, grouping sensor readings by moisture level
(where was expected to have reading not differing within the
same moisture, regardless the soil texture); all three sensors
were able to read 15% moisture for all three textures (did not
differ significantly), but at different voltage for each texture,
indicating that even when they translate well the moisture
(into voltage), the use of a single calibration model
disregarding the soil texture, is a mistake.
As the moisture rises, the sensors’ readings decrease the
differences within texture but kept the difference between
moisture, as with 52% they were able to have the same
reading for sand and medium texture, but not for clay. Thus,
the sensor could have a suitable performance, once calibrated
to the soil.
When calibrating and building the model, it was verified
that the voltage values presented an inverse relation to the
soil moisture, which was already expected, since low voltage
values are associated with low values of resistance to the
passage of electric current in the soil. Results obtained by
Vernandhes et al. (2017) corroborated this finding.
The R² values obtained by the calibration curves (0.841,
0.858 and 0.872) indicated an efficient calibration, validating
the calibration of these sensors. PAYERO et al. (2017), using
Decagon EC-5 model soil moisture sensors, which have a
working principle based on the measurement of soil dielectric
constant using capacitance technology, performed the
calibration for a soil type, resulting in equal R² to 0.999. A
similar result was found for the R² of the calibration equation,
which uses the mean values obtained by the sensors.
The RMSE values for the sensors were 0.665; 0.516; and
0.556 for sandy, medium texture and clay soils, respectively.
The values found were relatively high, as there are
commercial sensors with errors lower than 3%, such as the
Fieldscout 350 TDR probe (manufacturer Spectrum
Technologies, Inc.). Such results cast doubt on the accuracy
and reliability of the sensors studied in this work.
Therefore, the use of sensors should be restricted to
operations that do not require high accuracy (i.e. lower than
10%). In addition, the sensors should not be used in
experiments that require data acquisition in several locations
at the same time, since this procedure requires individual
calibration, which would make the procedure extremely time
consuming.
5. CONCLUSIONS
The evaluated sensors did not present good precision to
estimate soil moisture content without previous calibration
procedure.
Soil texture directly influenced the readings obtained by
the sensors at a given value of moisture content, so – once
calibrated for the soil texture – more accurate measurements
could be expected.
Low and dry soils led to a better sensor performance
(above 50% moisture conditions). Therefore, it’s important
to have a prior knowledge of the soil type in which the sensor
will be used. The lack of information can lead to misleading
information, resulting in waste of resources or an inefficient
irrigation.
The evaluated sensors did not present replicability, so it
is necessary to calibrate each sensor individually. Therefore,
the use of this sensor model for monitoring soil conditions
in larger areas is not feasible (due to soil texture variation and
large moisture range).
Despite its low cost, flexibility and fast data acquisition,
this sensor model requires a time-consuming calibration
procedure.
6. ACKNOLEDGEMENT
The authors thank Professor Dario de Andrade Prata
Filho for allowing the use of the LaDISan laboratory where
the experiment was developed and for his aid to the research.
The authors would like to thank the research funding
agencies CAPES and FAPEMIG for the scholarships
granted to the post-graduate students participating in the
study.
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