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This paper presents a spectral band selection method for feature dimensionality reduction in hyperspectral image analysis for detecting skin tumors on poultry carcasses. A hyperspectral image contains spatial information measured as a sequence of individual wavelength across broad spectral bands. Despite the useful information for skin tumor detection, real-time processing of hyperspectral images is often a challenging task due to the large amount of data. Band selection finds a subset of significant spectral bands in terms of information content for dimensionality reduction. This paper presents a band selection method of hyperspectral images based on the recursive divergence for the automatic detection of poultry carcasses. For this, we derive a set of recursive equations for the fast calculation of divergence with an additional band to overcome the computational restrictions in real-time processing. A support vector machine is used as a classifier for tumor detection. From our experiments, the proposed band selection method shows high detection accuracy with low false positive rates compared to the canonical analysis at a small number of spectral bands. Also, compared with the enumeration approach of 93.75% detection rate, our proposed recursive divergence approach gives 90.6% detection rate, which is within the industry-accepted accuracy of 90-95%, while achieving the computational saving for real-time processing.