Using a region-based convolutional neural network (R-CNN) for potato segmentation in a sorting process

Jaka Verk (Author), Jernej Hernavs (Author), Simon Klančnik (Author)

Abstract

This study focuses on the segmentation part in the development of a potato-sorting system that utilizes camera input for the segmentation and classification of potatoes. The key challenge addressed is the need for efficient segmentation to allow the sorter to handle a higher volume of potatoes simultaneously. To achieve this, the study employs a region-based convolutional neural network (R-CNN) approach for the segmentation task, while trying to achieve more precise segmentation than with classic CNN-based object detectors. Specifically, Mask R-CNN is implemented and evaluated based on its performance with different parameters in order to achieve the best segmentation results. The implementation and methodologies used are thoroughly detailed in this work. The findings reveal that Mask R-CNN models can be utilized in the production process of potato sorting and can improve the process.

Keywords

segmentacija slike;sortiranje krompirja;nevronske mreže;maska RCNN;odkrivanje predmetov;proizvodni procesi;strojno učenje;umetna inteligenca;image segmentation;potato sorting;neural network;mask RCNN;object detection;production process;machine learning;AI;

Data

Language:	English
Year of publishing:	2025
Typology:	1.01 - Original Scientific Article
Organization:	UM FS - Faculty of Mechanical Engineering
Publisher:	MDPI
UDC:	681.5:004.8
COBISS:	230214403
ISSN:	2304-8158
Views:	0
Downloads:	9
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	segmentacija slike;sortiranje krompirja;nevronske mreže;maska RCNN;odkrivanje predmetov;proizvodni procesi;strojno učenje;umetna inteligenca;
Type (COBISS):	Article
Pages:	18 str.
Volume:	ǂVol. ǂ14
Issue:	ǂiss. ǂ7, [article no.] 1131
Chronology:	March 2025
DOI:	10.3390/foods14071131
ID:	26119011