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undergraduate thesis
Raspoznavanje bolesti lista biljke uporabom strojnog učenja na temelju slike
2020. urn:nbn:hr:200:425249
Radočaj, Petra
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Software Engineering
Chair of Programming Languages and Systems

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Radočaj, P. (2020). Raspoznavanje bolesti lista biljke uporabom strojnog učenja na temelju slike (Undergraduate thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek. Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:425249

Radočaj, Petra. "Raspoznavanje bolesti lista biljke uporabom strojnog učenja na temelju slike." Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2020. https://urn.nsk.hr/urn:nbn:hr:200:425249

Radočaj, Petra. "Raspoznavanje bolesti lista biljke uporabom strojnog učenja na temelju slike." Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2020. https://urn.nsk.hr/urn:nbn:hr:200:425249

Radočaj, P. (2020). 'Raspoznavanje bolesti lista biljke uporabom strojnog učenja na temelju slike', Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 19 April 2024, https://urn.nsk.hr/urn:nbn:hr:200:425249

Radočaj P. Raspoznavanje bolesti lista biljke uporabom strojnog učenja na temelju slike [Undergraduate thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2020 [cited 2024 April 19] Available at: https://urn.nsk.hr/urn:nbn:hr:200:425249

P. Radočaj, "Raspoznavanje bolesti lista biljke uporabom strojnog učenja na temelju slike", Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:200:425249

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Metadata
TitleRaspoznavanje bolesti lista biljke uporabom strojnog učenja na temelju slike
Title (english)Plant leaf disease recognition using image-based machine learning
AuthorPetra Radočaj
MentorZdravko Krpić (mentor)
MentorBruno Zorić (sumentor)
Committee memberZdravko Krpić (predsjednik povjerenstva)
Committee memberDanijel Topić (član povjerenstva)
Committee memberEmmanuel Karlo Nyarko (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Software Engineering)
(Chair of Programming Languages and Systems)
Osijek
Defense date and country2020-09-16, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Artificial Intelligence
Abstract
U teorijskom dijelu ovoga rada opisan je problem raspoznavanja bolesti lista biljke uz prikaz mogućnosti primjene postupaka dubokog strojnog učenja. Razvoj modela za klasifikaciju bolesti potaknuo je unaprjeđenje automatiziranih sustava za otkrivanje biljnih bolesti pomoću vidljivih simptoma na lišću dok je sam proces izdvajanja značajki ugrađen u algoritam. Znanje o ovom procesu značajno bi doprinijelo području znanosti pri razumijevanju dijagnostičkih postupaka i detekciji bolesti. U eksperimentalnom dijelu rada proveden je postupak klasifikacije bolesti lista biljke na prerađenom GooLeNet i VGGNet modelu te MobileNet, InceptionV3 i InceptionResNetV2 mrežnim arhitekturama. Sve konvolucijske neuronske mreže trenirane su i testirane na odabranom skupu podataka koji uključuje bolesne i zdrave jedinke kukuruza podijeljene u četiri klase. Najpreciznije rezultate omogućio je InceptionResNetV2 model s 98.9% točnosti na odabranom skupu podataka.
Abstract (english)
The problem of recognizing plant leaf diseases with a review of the current possibilities by applying deep machine learning is described in the theoretical part of this thesis. The development of disease classification models has prompted improvements of automated plant disease detection systems using visible symptoms, while the feature extraction is built in the algorithms. Knowledge of this process has contributed to the field of science in understanding diagnostic procedures and disease detection. In the experimental part of the thesis, the procedure of leaf diseases classification with the modified GoogleNet and VGGNet model MobileNet, alongside InceptionV3 and InceptionResNetV2 network architectures was performed. All convolutional neural networks were trained and tested on selected set data that included diseased and healthy maize images divided into four classes. The most accurate results were achieved by the InceptionResNetV2 model with 98.9% accuracy on the selected data set.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:425249
Study programmeTitle: Undergraduate University Study Programme in Computer Engineering
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2020-11-17 11:39:53