Development of a Hyperspectral Computer Vision System Based on Two Liquid Crystal Tuneable Filters for Fruit Inspection. Application to Detect Citrus Fruits Decay
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Hyperspectral systems are characterised by offering the possibility of acquiring a large number of images at different consecutive wavebands. To ensure reliable and repeatable results using this kind of optical sensors, the intensity shown by the objects in the different spectral images must be independent from the differences in sensitivity of the system for the different wavelengths. The spectral efficiency of the acquisition devices and the spectral emission of the lighting system vary across the spectrum and the images, and therefore the results can reproduce these variations if the system is not properly calibrated and corrected. This is particularly complex, when several LCTF devices are used to obtain large spectral ranges. This work presents the development of a hyperspectral system based on two liquid crystal tuneable filters for the acquisition of images of spherical fruits. It also proposes a methodology for acquiring and segmenting images of citrus fruits aimed at detecting decay in citrus fruits that has been capable of correctly classifying 98 % of pixels as rotten or non-rotten and 95 % of fruit.
KeywordsHyperspectral Citrus fruits Decay detection Fruit inspection Artificial neural networks
This work has been partially funded by the Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria de España (INIA) through research project RTA2012-00062-C04-01 and RTA2012-00062-C04-03 with the support of European FEDER funds, the Universitat de València through project UV-INV-AE11-41271, and the UPV-IVIA through collaboration agreement UPV-2013000005.
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