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This paper reports the results of waveband selection for detecting internal insect infestation in tart cherries as a precursor to development of a dedicated multispectral vision system. A genetic algorithm (GA) approach was applied on hyperspectral transmittance images (580-980 nm) and reflectance spectral data (590-1,550 nm) acquired from both intact and infested tart cherries. The GA analysis indicates that the ability of using transmittance imaging approach for detecting internal insect infestation in tart cherries would be limited. According to the GA analysis on the reflectance spectra, visible wavelengths were of less importance than NIR wavelengths for the purpose of distinguishing intact cherries from infested ones. The PLSDA results indicate that models built with three or four GA selected wavelength regions gave similar classification accuracy to the model built with full wavelength region, which demonstrates the efficiency of the GA variable selection procedure. However, due to the stochastic nature of the GA, the efficiency of using these wavebands in a multispectral vision system needs to be verified in future work.

This article reports on the development of a hyperspectral imaging prototype for online evaluation of external and internal quality of pickling cucumbers. The prototype consisted of a two-lane round belt conveyor, two illumination sources (one for reflectance and one for transmittance), and a hyperspectral imaging unit. It had a novel feature of simultaneous imaging under reflectance mode in the visible region (400-675 nm) and transmittance mode for the red and near-infrared region (Red-NIR) (675-1000 nm). Reflectance information from the visible region was intended for evaluating the external characteristics of cucumbers such as skin color, whereas transmittance information from Red-NIR was used for internal defect detection (i.e., hollow center). Additional features of the prototype included simultaneous acquisition of reflectance and transmittance from calibration references that were installed in the system, to provide real-time, continuous corrections of individual hyperspectral images from each sample. Methods and algorithms were developed of estimating cucumber fruit size and correcting the effect of fruit size on transmittance measurements. The system was calibrated and evaluated for detecting the color, size, and internal defect of pickling cucumbers.

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