RESUMEN
Automatic detection of weeds is necessary for site--specific application of herbicides or precise physical weed control. Leaf reflectance is mainly determined by photosynthetic pigments, leaf structural properties and water content, so spectral reflectance characteristics can be used for weed discrimination. The spectral reflectance of cotton, rice and weeds was determined in the range from 350 to 2 500 nm using the Analytical Spectral Device Full Range FieldSpec Pro (ASD) in laboratory. The discrimination analysis was done using the statistical software package SAS. The characteristic wavelengths were selected by using STEPDISC procedure. With the selected characteristic wavelengths, discriminant models were developed using the DISCRIM procedure in SAS. For distinguishing spine-greens from cotton, three characteristic wavelengths, 385, 415, and 435 nm, were selected, and good classification performance (100% accuracy) was achieved. The combination of characteristic wavelengths 415 and 435 nm has the biggest contribution to discrimination model. For distinguishing barnyard-grass from rice, five characteristic wavelengths, 375, 465, 585, 705, and 1 035 nm, were selected, and also good classification performance (100% accuracy) was obtained. The transition point from yellow to orange wavelength (585 nm) and the wavelength 705 nm in the red edge contributed more to discrimination model.