Praćenje leta maslininog moljca razvojem i korištenjem modela umjetnih neuronskih mreža

Zdrilić, Anđelo

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master's thesis

Praćenje leta maslininog moljca razvojem i korištenjem modela umjetnih neuronskih mreža

Zagreb: University of Zagreb, Faculty of Agriculture, 2023. urn:nbn:hr:204:866325

Zdrilić, Anđelo

University of Zagreb
Faculty of Agriculture
Agricultural Zoology

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APA 6th Edition

Zdrilić, A. (2023). Praćenje leta maslininog moljca razvojem i korištenjem modela umjetnih neuronskih mreža (Master's thesis). Zagreb: University of Zagreb, Faculty of Agriculture. Retrieved from https://urn.nsk.hr/urn:nbn:hr:204:866325

MLA 8th Edition

Zdrilić, Anđelo. "Praćenje leta maslininog moljca razvojem i korištenjem modela umjetnih neuronskih mreža." Master's thesis, University of Zagreb, Faculty of Agriculture, 2023. https://urn.nsk.hr/urn:nbn:hr:204:866325

Chicago 17th Edition

Harvard

Zdrilić, A. (2023). 'Praćenje leta maslininog moljca razvojem i korištenjem modela umjetnih neuronskih mreža', Master's thesis, University of Zagreb, Faculty of Agriculture, accessed 28 December 2024, https://urn.nsk.hr/urn:nbn:hr:204:866325

Vancouver

Zdrilić A. Praćenje leta maslininog moljca razvojem i korištenjem modela umjetnih neuronskih mreža [Master's thesis]. Zagreb: University of Zagreb, Faculty of Agriculture; 2023 [cited 2024 December 28] Available at: https://urn.nsk.hr/urn:nbn:hr:204:866325

IEEE

A. Zdrilić, "Praćenje leta maslininog moljca razvojem i korištenjem modela umjetnih neuronskih mreža", Master's thesis, University of Zagreb, Faculty of Agriculture, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:204:866325

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Title	Praćenje leta maslininog moljca razvojem i korištenjem modela umjetnih neuronskih mreža
Title (english)	Monitoring of the flight of olive kernel borer by developing and using model based on artificial neural networks
Author	Anđelo Zdrilić
Mentor	Tomislav Kos (mentor)
Mentor	Ivana Pajač Živković (komentor)
Committee member	Tomislav Kos (predsjednik povjerenstva)
Committee member	Darija Lemić (član povjerenstva)
Committee member	Martina Skendrović Babojelić (član povjerenstva)
Granter	University of Zagreb Faculty of Agriculture (Agricultural Zoology) Zagreb
Defense date and country	2023, Croatia
Scientific / art field, discipline and subdiscipline	BIOTECHNICAL SCIENCES Agronomy
Abstract	Maslinin moljac (Prays oleae Bernard, 1788) jedan je od najznačajnijih štetnika masline. U maslinicima radi štete velikih gospodarskih razmjera, najčešće u obliku opadanja plodova, slabijem urodu i sporijem rastu stabla. Kako bi se njegova prisutnost na terenu ranije uočila, predložen je razvoj modela za automatsku detekciju temeljen na prepoznavanju sadržaja slika pomoću umjetnih neuronskih mreža. Postavljanjem lovki s feromonima, prikupljanjem i fotografiranjem uzoraka, na lovkama ulovljenih, odraslih oblika P. oleae na ljepljivim pločama, anotiranjem fotografija i učenjem neuronske mreže, razvijen je model za automatsko praćenje ovog štetnika. Osim vrste P. oleae, model je naučen prepoznavati ostale kukce kao i ostale elemente na slikama. Preciznost prepoznavanja odraslih oblika moljca iznosila je 59 % u odnosu na vizualni pregled, a veća je od preciznosti prepoznavanja ostalih štetnika i ostalih elemenata na fotografijama ljepljivih ploča.
Abstract (english)	Olive kernel borer (Prays oleae Bern ard, 1788) is one of the most significant pests of olive tree. In olive groves, it can cause damage of large economic proportions , commonly in form of fruit drop, weaker yield, or stagnant tree growth . To detect its presence in the field as soon as possible, an automatic model based on the image recognition of image content using artificial neural networks is used. By setting up phe romone based traps, collecting and photographing samples of adult P. oleae, annotating photographs and teaching a neural network to use them, an automatic model is developed for tracking olive kernel borer individuals. Besides the species P. oleae , the mod el was taught to recognize other pests as well as other elements of proposed images. The accuracy of recognition of the adult moths was 59 compared to visual inspection of proposed photographs and was higher that the accuracy of recognition of other pest s and other elements on photographs of adhesive panels.
Keywords
Keywords (english)
Language	croatian
URN:NBN	urn:nbn:hr:204:866325
Study programme	Title: Mediterranean Agriculture Study programme type: university Study level: graduate Academic / professional title: magistar/magistra inženjer/inženjerka mediteranske poljoprivrede (magistar/magistra inženjer/inženjerka mediteranske poljoprivrede)
Type of resource	Text
File origin	Born digital
Access conditions	Open access Embargo expiration date: 2024-11-08
Terms of use
Repository	Repository Faculty of Agriculture University of Zagreb
Created on	2023-11-08 11:15:42