Citrus black spot detection using hyperspectral imaging

Daegwan Kim, Thomas F. Burks, Mark A. Ritenour, Jianwei Qin

Abstract


Abstract: This paper describes the development of a hyperspectral imaging approach for identifying fruits infected with citrus black spot (CBS). Hyperspectral images were taken of healthy fruit and those with CBS symptoms or other potentially confounding peel conditions such as greasy spot, wind scar, or melanose. Spectral angle mapper (SAM) and spectral information divergence (SID) hyperspectral analysis approaches were used to classify fruit samples into two classes: CBS or non-CBS. The classification accuracy for CBS with SAM approach was 97.90%, and 97.14% with SID. The combination of hyperspectral images and two classification approaches (SID and SAM) have proven to be effective in recognizing CBS in the presence of other potentially confounding fruit peel conditions. The study result can be a reference for the non-destructive detection of fruits infected with citrus black spot.
Keywords: citrus black spot, hyperspectral imaging, spectral angle mapper, spectral information divergence, imaging processing
DOI: 10.3965/j.ijabe.20140706.004

Citation: Kim D, Burks T F, Ritenour M A, Qin J W. Citrus black spot detection using hyperspectral imaging. Int J Agric & Biol Eng, 2014; 7(6): 20－27.

Keywords


citrus black spot, hyperspectral imaging, spectral angle mapper, spectral information divergence, imaging processing

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