Remote Sensing in agriculture makes possible the acquisition of large amount of data without physical contact, providing diagnostic tools with important impacts on costs and quality of production. Hyperspectral imaging sensors attached to airplanes or unmanned aerial vehicles (UAVs) can obtain spectral signatures, that makes viable assessing vegetation indices and other characteristics of crops and soils. However, some of these imaging technologies are expensive and therefore less attractive to familiar and/or small producers. In this work a method for estimating Near Infrared (NIR) bands from a low-cost and well-known RGB camera is presented. The method is based on a weighted sum of NIR previously acquired from pre-classified uniform areas, using hyperspectral images. Weights (belonging degrees) for NIR spectra were obtained from outputs of K-nearest neighbor classification algorithm. The results showed that presented method has potential to estimate near infrared band for agricultural areas by using only RGB images with error less than 9%.