Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems

Abstract

The results demonstrated that these carefully-chosen wavebands are more informative than RGB images alone and enable early identification of charcoal rot infection in soybean. The selected wavebands could be used in a multispectral camera for remote identification of charcoal rot infection in soybean.