ExG-ExR index of the RGB image. In red color, the greatest photosynthetic activity.
In this case I tried to test if the Nduino can make some measurements that could be related to those carried out with the N-tester
. To simplify the tests I used leaves that I had on hand, such as Ficus benjamina
, Robinia pseudoacacia
and Solanum jasminoides
. I used leaves in various stages of senescence. 18 samples were used in the first case, 10 in the second and 12 in the third. I understand it's not a very generous sampling, but for a first approximation can serve. With the Nduino I took the mean of 3 samples per leaf, and for N-tester the average of 30 samples per leaf were taken as it is required by the device to give a measurement.
Simple linear regression between the measurements of several indices RGB vegetation and N-tester was conducted
. I will not go into depth on statistical results. As a simply summary, in the case of ficus
, the best index was EXG-ExR
(-0.84 correlation coefficient and R-square of 70.4%, with a P value of 0.0000). For Robinia
, the better index was EXG-ExR
too, with a correlation coefficient of 0.79, an R-square of 63.0% and a P value of 0.0062. Finally, in the case of Solanum jasminoides
the index with better behavior was ExG
, with a correlation coefficient of -0.92 and an R-square of 84.4%. As in the first case the P value was 0.0000. The ExG-ExR
index in this last case gave a correlation coefficient of 0.71 and an R-squared of 50.63% with a P 0,0095.
What can we conclude from these first tests?
First, it is necessary to take more samples and measurements in crops with agricultural value
(corn, beet, ...).
Second, the tests data has to be compared with NDVI measurements obtained from aerial images
(from a drone, for example) and do a deeper statistical analysis to develop models
of interpretation of the indices for these crops, equivalent to those offered with the N-tester, but preferably associated with a GIS.
In any case, it seems clear that the relationship between measurements exist
, which may be of interest in those places where there is a need to adjust fertilization and is not possible, for economic reasons, using measurements with N-tester, drones , satellites, etc
. For example, in developing countries. We can think in a device that was calibrated for a specific crop, and can give a simple orientation of fertilization per hectare in each area of a plot
, using a device like this and a GIS open as QGis or Grass or an online GIS designed for this purpose, for example. In the same way, it could be applied to the colorimetric measurement of fruits, grapes, olives
, ... so that if we find the right mathematical relationship, we could organize the harvest by qualities
The equipment can be miniaturized
, as there are quite smaller designs than Uno
. And its low cost can make it accessible through NGOs to farmers in developing countries
, for example. So the idea is simple but applications are promising. We try to make them happen.