01980naa a2200301 a 450000100080000000500110000800800410001902400610006010000200012124500770014126000090021852010850022765000150131265000260132765000140135365000290136765000210139665000140141765300260143165300290145765300180148665300290150470000210153370000260155470000230158070000230160377300520162621172312021-02-17       2020    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1016/j.biosystemseng.2019.11.0232DOI1 aSOUZA, M. F. de  aSpectral differentiation of sugarcane from weeds.h[electronic resource]  c2020  aSite-specific application of herbicides is highly desirable for optimising its usage and reducing environmental damages. Thus, developing techniques for identification and mapping of weeds is necessary for a proper precision agriculture adoption. Such weed identification for site-specific management is difficult when the main crop is already established in the field. This study shows the possibility of differentiating sugarcane plants from weeds by the spectral behaviour of the leaves. The performance of two modelling methods, SIMCA (soft independent modelling by class analogy) and the RF algorithm (random forest) was compared. The simplification of the Vis-NIR spectrum into only four bands of interest (500e550 nm; 650e750 nm; 1300e1450 nm; and 1800e1900 nm) was verified by demonstrating they had the same differentiation ability as the full visible-near infra-red spectrum. Thus, it was shown that performing the proper band selection and local calibration using a spectral classification approach may allow weed mapping and facilitate localised herbicide application.  aHerbicides  aPrecision agriculture  aSugarcane  aAgricultura de Precisão  aCana de Açúcar  aHerbicida  aClassification models  aPost-emergent herbicides  aRandom forest  aSite-specific management1 aAMARAL, L. R. do1 aOLIVEIRA, S. R. de M.1 aCOUTINHO, M. A. N.1 aFERREIRA NETTO, C.  tBiosystems Engineeringgv. 190, p. 41-46, 2020.