Raspberry quality detection in visual spectrum using neural networks

Blagodyr, Andrii

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Факультет прикладних наук
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2021
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dc.contributor.author	Blagodyr, Andrii
dc.date.accessioned	2021-06-25T12:27:58Z
dc.date.available	2021-06-25T12:27:58Z
dc.date.issued	2021
dc.identifier.citation	Blagodyr, Andrii. Raspberry quality detection in visual spectrum using neural networks: manuscript rights/ Andrii Blagodyr; Supervisor: Viktor Sakharchuk; Ukrainian Catholic University, Department of Computer Sciences. – Lviv : [s.n.], 2021. – 59 p. : ill.	uk
dc.identifier.uri	https://er.ucu.edu.ua/handle/1/2696
dc.language.iso	en	uk
dc.subject	berry fruits quality detection	uk
dc.subject	CNN approach	uk
dc.subject	NN application	uk
dc.subject	convolutional neural network	uk
dc.subject	U-net architecture	uk
dc.subject	PSPnet architecture	uk
dc.title	Raspberry quality detection in visual spectrum using neural networks	uk
dc.type	Preprint	uk
dc.status	Публікується вперше	uk
dc.description.abstracten	The thesis presents the raspberry quality detection approach based on a convolutional neural network with U-net architecture and compared with PSPnet architecture. The limited possibility to use manual labour when growing, sorting, processing vegetables, fruits and berries in the face of increasing risks of new pandemics determines the study’s relevance. For the research, a neural network of the U-net architecture has been chosen based on the narrow focus of the task and small repetitive patterns. The neural network of the U-net architecture has proven itself well in solving problems of image segmentation in biomedical researches. Therefore, the author decided to expand the scope of this tool to a new area of investigation. The research is carried out on the data that the researchers have collected for the experiment. The dataset for the experiment has been generated manually based on images of different varieties of raspberries and various states of raspberry fruits. This research is expected to become a part of the complex robotic system for solving the problem of manual berry fruits sorting.	uk