The use of computer vision to classify Xaraés grass according to nutritional status in nitrogen



Image processing. Remote sensing. HSB. Spectral signature.


This study is based on the principle that vegetation indexes (VIs), derived from the RGB color model obtained
from digital images, can be used to characterize spectral signatures and classify Brachiaria brizantha cv. Xaraés according to
nitrogen status (N). From colorimetric data obtained from leaf blade images acquired in the fi eld, three artifi cial neural networks were
evaluated according to the performance in the classifi cation of N status: Feedforward Backpropagation (FFBP), Cascade Forward
Backpropagation (CFBP) and Radial Base function (RBFNN). Four N fertilization rates were applied to generate contrasting N contents in the
plants. The youngest completely expanded leaves from 60 tillers were detached at each regrowth cycle of 28 days, thus their images and leaf N
content were obtained. Samples were then classifi ed as defi cient (< 17 g N kg-1 leaf dry matter (DM), moderately defi cient (from 17.1
to 20.0 g N kg-1 DM), and suffi cient (> 20.1 g N kg-1 DM). The VIs were selected by principal component analysis and the performance
of the networks evaluated by the accuracy. The accuracy in classifi cation obtained by the networks were 88%, 86% and 79% for FFBP,
CFBP and RBFNN, respectively, indicating that the spectral signatures can be determined from images acquired in the fi eld. So, the
proposed method could be used to develop a software that aims to monitor the status of N in real time, providing a fast and inexpensive
tool for defi ning the time and the amount of N fertilizer, according to the pasture demand.