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Title:Nadzor kakovosti pšeničnih zrn z naprednimi postopki računalniške obdelave slik in razpoznavanja vzorcev
Authors:ID Krpan, Blaž (Author)
ID Potočnik, Božidar (Mentor) More about this mentor... New window
Files:.pdf MAG_Krpan_Blaz_2015.pdf (2,78 MB)
MD5: 2CD510414BAA30144DCB339B07F800E8
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Pšenica je ena izmed najbolj priljubljenih žitaric v prehrambni industriji, zato je nadzor kakovosti pšenice ključnega pomena. V okviru magistrske naloge smo pokazali, da je mogoče proces nadzora kakovosti pšenice avtomatizirati in izboljšati v primerjavi z današnjim pristopom. Reševanja problema smo se lotili z računalniško analizo digitalnih slik in spektralnih odzivov zrn ter uporabo algoritmov strojnega učenja. Posebno pozornost smo posvetili združevanju obeh tipov podatkov. Prišli smo do zaključka, da lahko z natančnostjo 90 % ali več razvrstimo zrna zdrave pšenice in ostalih 8 razredov, ki predstavljajo poškodovana zrna in druga žita. Za potrditev rezultatov in izboljšanje robustnosti algoritma predlagamo še dodatne eksperimente.
Keywords:nadzor kakovosti, pšenica, strojno učenje, razvrščanje, obdelava slik
Place of publishing:[Maribor
Publisher:B. Krpan
Year of publishing:2015
PID:20.500.12556/DKUM-55117 New window
UDC:004.93'1:633.11(043.2)
COBISS.SI-ID:19317014 New window
NUK URN:URN:SI:UM:DK:EJVBLIFJ
Publication date in DKUM:15.10.2015
Views:1320
Downloads:132
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
:
KRPAN, Blaž, 2015, Nadzor kakovosti pšeničnih zrn z naprednimi postopki računalniške obdelave slik in  razpoznavanja vzorcev [online]. Master’s thesis. Maribor : B. Krpan. [Accessed 23 March 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=55117
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Secondary language

Language:English
Title:Wheat grains quality control with advanced methods for image processing and pattern recognition
Abstract:Wheat is one of the most produced cereals for food industry worldwide. Therefore quality control of wheat is an essential part of the supply chain. In the thesis, it has been shown that it is possible to automatize and improve the wheat quality inspection procedures used today. To solve the problem digital images and spectral responses of the grains have been analysed in combination with the machine learning algorithms. Additional effort has been spent on combining the two types of data. Conclusion has been made that it is possible to distinguish between the healthy wheat and the other 8 classes, which represent damaged grains and other cereals, with the accuracy of 90% or more. In order to confirm the results and improve the robustness of the developed algorithm further experiments shall be made.
Keywords:quality inspection, wheat, machine learning, classification, image processing


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