Hyperspectral imagery deals with large volumes of data due to hundreds of spectral bands used in the images. Although hyperspectral images with higher spectral resolution usually carry more information, the processing of the images requires a significant amount of memory and is usually very slow. In addition, hyperspectral images contain considerable amount of redundant information, which does not help or even hinder the algorithm in making the correct decision. Band selection is an approach to both reduce the dimensionality of hyperspectral images and save calculation time for further applications, such as detection of blueberry fruit with different maturity stages. Hyperspectral images of blueberry fruit were taken in a commercial blueberry field. Mature fruit, intermediate fruit, young fruit and background were the four classes to be studied. A supervised band selection method was proposed using Kullback-Leibler divergence (KLD). Wider bands were made by combining 20 hyperspectral bands so that the selected bands could be used in a blueberry yield mapping system using a lower-cost multispectral camera. Based on the analysis, six combined bands were selected: 543.1-572.6 nm, 627.4-658.8 nm, 663.6-695.2 nm, 725.4-757.4 nm, 773.5-805.6 nm and 838-870.5 nm. The test result showed that the proposed band selection method worked well for the task of blueberry growth stages detection.