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Title Multispectral Analysis of Seeds
Author Jensen, Anders Boeck
Supervisor Carstensen, Jens Michael (Image Analysis and Computer Graphics, Department of Informatics and Mathematical Modeling, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)
Institution Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark
Thesis level Master's thesis
Year 2009
Abstract This thesis presents work done with multispectral image analysis of seeds with the goal of finding fungus infections on the seeds. The goal is to determine if a seed is infected and if so with which fungi it infected, and to what degree it is infected. Besides the work with the seeds, it is also tested if it is possible to remove camera refl ections from a multispectral image. The main approach used is spectral unmixing which decomposes the image into a number of endmembers, each corresponding to clean seed or the fungi. 5 different fungi were chosen for this study. With the decomposition specific fungus infection on the seed may be determined. The image data are extended with morphological closing and Laplace filtering with the aim of getting better results. Statistical classification is also used to identify the fungi in order to compare spectral unmixing with a simple method. The result with removing camera re flection shows that spectral unmixing is not suited for this task. The camera re flection can be identified in the images, but not removed. When applying spectral unmixing on the seed data, it is possible to find out if a seed is infected or not. Using only the image data, however, it is not possible to identify which fungus is infecting the seed. When also using an extension based on morphological closing, it is possible to identify 3 of the 5 fungi studied. This makes it possible to determine which fungus a whole seed is infected with if it only has one infection, but the result was not good enough to determine the fungi at a pixel level. Classification was able to identify all fungi, but once more, not at a pixel level. With spectral unmixing it is possible to determine the degree of infection, while this is not possible with classification. The results of this thesis show that it is possible to identify the fungi with both spectral unmixing and classification, and they also show that spectral unmixing is not superior to classification. More work is needed for both techniques to work in practical applications.
Imprint Technical University of Denmark (DTU) : Kgs. Lyngby, Denmark
Series IMM-M.Sc.-2009-12
Fulltext
Original PDF ep09_12_net.pdf (25.51 MB)
Admin Creation date: 2009-03-11    Update date: 2009-11-04    Source: dtu    ID: 239915    Original MXD