This study aimed to develop classifiers based on different combinations of spectral bands and vegetation indices from original, segmented and reflectance images in order to determine the levels of leaf nitrogen and chlorophyll in the bean, and to define the best time and best variables. A remote-sensing system was used, consisting of a helium balloon and two small-format digital cameras. Besides the individual spectral bands, four vegetation indices were tested: simple ratio, normalized difference, normalized difference in the green band, and modified-chlorophyll absorption. The classifiers proved to be efficient in determining levels of leaf nitrogen and chlorophyll. The best time for determining leaf N content was at 13 DAE (stage V4). The best classifiers for that time used as input variables two indices from segmented reflectance images, one index related to the canopy structure and the other related to chlorophyll, with a Kappa ranging from 0.26 to 0.31. The best time to discriminate leaf chlorophyll content was 21 DAE (stage V4). The best classifier used as input variables two original images, one in the red band and one in the blue with a Kappa of 0.47.
Precision farming; Remote sensing; Nitrogen dosage
