Cecid fly defect detection in mangoes using object detection frameworks

Simple item page
creativework.keywordsConvolutional neural networks, defect detection, Image processing, Region-based CNN
dc.contributor.authorBaculo, Maria Jeseca C. an
dc.contributor.authorRuiz, Conrado
dc.contributor.authorAran, Oya
dc.date.accessioned2026-09-10T08:04:48Z
dc.date.available2026-09-10T08:04:48Z
dc.date.issued2021
dc.descriptionAbstract only
dc.description.abstractMango export has experienced rapid growth in global trade over the past few years, however, they are susceptible to surface defects that can affect their market value. This paper investigates the automated detection of a mango defect caused by cecid flies, which can affect a significant portion of the production yield. Object detection frameworks using CNN were used to localize and detect multiple defects present in a single mango image. This paper also proposes modified versions of R-CNN and FR-CNN replacing its region search algorithms with segmentation-based region extraction. A dataset consisting of 1329 cecid fly surface blemishes was used to train the object detection models. The results of the experiments show comparable performance between the modified and existing state-of-the-art object detection frameworks. Results show that Faster R-CNN achieved the highest average precision of 0.901 at aP50 while the Modified FR-CNN has the highest average precision of 0.723 at aP75.
dc.identifier.citationBaculo, M. J. C., Ruiz C., Jr & Aran O. (2019). Cecid fly defect detection in mangoes using object detection frameworks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13002 LNCS, 205 – 216. 10.1007/978-3-030-89029-2_16.
dc.identifier.doi10.1007/978-3-030-89029-2_16
dc.identifier.isbn978-303089028-5
dc.identifier.issn03029743
dc.identifier.urihttps://lakasa.dmmmsu.edu.ph/handle/123456789/2489
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.rights.licenseCC BY 4.0
dc.sdgSDG 2
dc.sdgSDG 8
dc.sdgSDG 12
dc.subjectNeural networks (Computer science)
dc.subjectDefectors
dc.subjectImage processing
dc.subjectImage processing--Digital techniques
dc.subjectImage analysis--Data processing
dc.subjectImage processing--Computer programs
dc.titleCecid fly defect detection in mangoes using object detection frameworks
dc.typeArticle
dcterms.accessRightsOpen access
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
PUB-JAPE-MLUC-2021- BaculoMJC-ABS.pdf
Size:
6.04 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
Journal articles published externally