Anomaly Detection in Fruits using Hyperspectral Images IJTSRD


One of the biggest problems in hyper spectral image analysis is the wavelength selection because of the immense amount of hypercube data. In this paper, we introduce an approach to find out the optimal wavelength selection in predicting the quality of the fruit. Hyper spectral imaging was built with spectral region of 400nm to 1000nm for fruit defect detection. For image acquisition, we used fluorescent light as the light source. Analysis was performed in visible region, which had spectral from 413nm to 642nm it was done because of the low reflectance spectrum found in fluorescent light sources. The captured image in this experiment demonstrates irregular illumination that means half of the fruit has brighter area. Analysis of the hyper spectral image was done in order to select diverse wavelengths that could possibly be used in multispectral imaging system. Selected wavelengths were used to create a separate image and each image went through thresholding. Experiment shows a multispectral imaging system which is able to detect defects in fruits by selecting most contributing wavelengths from the hyper spectral image. Algorithm presented in this paper could be improved with morphology operations so that we could get the actual size of the defect.

by Sandip Kumar | Parth Kapil | Yatika Bhardwaj | Uday Shankar Acharya | Charu Gupta “Anomaly Detection in Fruits using Hyper Spectral Images”

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019,

URL: https://www.ijtsrd.com/papers/ijtsrd23753.pdf

Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23753/anomaly-detection-in-fruits-using-hyper-spectral-images/sandip-kumar

call for paper Artificial Intelligence, international journal Bioinformatics, ugc approved journals Cognitive Science




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