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This article has been cited by other articles in ScienceCentral. A multispectral algorithm for detection and differentiation of defective defects on apple skin and normal Red Delicious apples was developed from analysis of a series of hyperspectral line-scan images.
A fast line-scan hyperspectral imaging system mounted on a conventional apple sorting machine was used to capture hyperspectral images of apples moving approximately 4 apples per second on a conveyor belt. The detection algorithm included an apple segmentation method and a threshold function, and was developed using three wavebands at nm, nm and nm.
The algorithm was executed on line-by-line image analysis, simulating online real-time line-scan imaging inspection during fruit processing.
Among the many apple varieties in the U. However, even with such a low tolerance, defective apples still present potential for causing foodborne illness. A variety of cases of foodborne illness related to contaminated fruits were reported in the U.
Automatic online detection of defective apples could help the industry to increase efficiency and reduce food safety risks. Several recent research studies have been carried out to develop image-processing methods for the detection of defective apples Bhatt et al.
Using processing and analysis methods for full-target images, these studies found that effective algorithms could be developed for non-destructive detection of defective apples using a variety of machine vision systems.
With a high-speed camera and an appropriate imaging spectrograph, a line-scan-based machine vision system can continuously acquire a series of line-scan images with full-spectrum data at each pixel on the line-scans.
Hyperspectral image analysis can be used to select specific essential wavebands relevant to the targeted inspection task from among hundreds of available wavebands Kim et al. The line-scan-based multispectral inspection function can then be implemented on a high-speed commercial food processing line by a line-scan machine vision system Kim et al.
The objective of this study is the development of a simple, line-scan-based multispectral algorithm to detect defects on Red Delicious apples and separate them from normal ones.
Guidelines for algorithm development included selection of a small number of wavebands, to support rapid data transfer and high-speed computation in a future real-world application, and more importantly, line-by-line image processing and analysis methods to ensure suitability for online line-scan inspection for high-speed apple processing operations.
Apples visually meeting with the requirements of the grades of U. Apples with clear injuries, damages by insects, internal breakdowns, or decays were classified as defective apples. This study used a total of Red Delicious apples—98 normal and 85 defective apples.
Light from a pair of watt quartz-tungsten halogen QTH lamps was channeled through fiber-optic line-light assemblies to provide illumination to the linear field of view FOV.
The machine vision system was installed to view apples in the conveyor lane of a commercial-type apple sorting machine FMC Corp, Philadelphia, PA. The sensing and illumination components were mounted within a black enclosure to eliminate the effects of ambient light.
To simplify image analysis, the apple-holding cups of the sorting machine were painted black for easy target segregation. Figure 1 shows a schematic of the of the line-scan machine vision system on the apple conveyor.
For a detailed description of the imaging system, readers are referred to an article by Kim et al. The general experimental design and major components of the hyperspectral line-scan imaging system.
Line-scan image acquisition For image acquisition, the apples were dumped into the loading end of the apple sorter so as to randomize the orientation of the apples in the conveyor cups as viewed by the overhead camera. Each line-scan image included spectral data along one axis and spatial data along another axis.
The spectrum for each pixel spanned nm to nm across 64 approx. Multiple series of line-scans were acquired to form hyperspectral images used for image analysis and algorithm development. These hyperspectral images contained sequences of either 9 to 10 normal apples or 5 defective apples, with approximately 80 to 90 line-scans required to complete the scan of the top-facing side of one apple not including the spaces between apples.A Simple Multispectral Imaging Algorithm for Detection of Defects on Red Delicious Apples.
The multispectral defect detection algorithm can potentially be used in commercial apple processing lines. Using the apple segemetation method, the multispectral algorithm based on the hyperspectral image data at nm, nm and nm quickly.
This paper is a review of optical methods for online nondestructive food quality monitoring. The key spectral areas are the visual and near-infrared wavelengths. We have collected the information of over papers published mainly during the last 20 years. Many of them use an analysis method called chemometrics which is shortly described in the paper.
From these studies, it appears that apple defect detection, especially for bicolour varieties, is a difficult task using standard image acquisition devices (colour or NIR cameras). On the other hand, economical and practical considerations must be taken into account in the .
MULTISPECTRAL IMAGE PROCESSING AND PATTERN RECOGNITION TECHNIQUES FOR QUALITY INSPECTION OF APPLE FRUITS Devrim Unay Members of the jury: Prof.
M. REMY (FPMs), President Prof. J. TRECAT (FPMs). A simple correlation analysis method using two-wavelength ratios and differences was used to find the best pair of wavelengths for differentiating between normal and defect apple regions. From the hyperspectral apple images, 66 and 54 representative spectra of defect regions and normal surface regions, respectively, were extracted.
Development of a Portable 3CCD Camera System for Multispectral Imaging of Biological Samples Hoyoung Lee 1, Soo Hyun Park 2, apple defect detection 1. Introduction hyperspectral-multispectral method has demonstrated the added benefit of software-selectable spectral.