Pest Detection in Olive Groves Using YOLOv7 and YOLOv8 Models

Alves, Adília; Pereira, J.A.; Khanal, Salik; Morais, A. Jorge; Filipe, Vitor

Publication

Pest Detection in Olive Groves Using YOLOv7 and YOLOv8 Models

2024Conference paper

dc.contributor.author	Alves, Adília
dc.contributor.author	Pereira, J.A.
dc.contributor.author	Khanal, Salik
dc.contributor.author	Morais, A. Jorge
dc.contributor.author	Filipe, Vitor
dc.date.accessioned	2024-10-09T09:21:43Z
dc.date.available	2024-10-09T09:21:43Z
dc.date.issued	2024
dc.description.abstract	Modern agriculture faces important challenges for feeding a fast-growing planet’s population in a sustainable way. One of the most important challenges faced by agriculture is the increasing destruction caused by pests to important crops. It is very important to control and manage pests in order to reduce the losses they cause. However, pest detection and monitoring are very resources consuming tasks. The recent development of computer vision-based technology has made it possible to automatize pest detection efficiently. In Mediterranean olive groves, the olive fly (Bactrocera oleae Rossi) is considered the key-pest of the crop. This paper presents olive fly detection using the lightweight YOLO-based model for versions 7 and 8, respectively, YOLOv7-tiny and YOLOv8n. The proposed object detection models were trained, validated, and tested using two different image datasets collected in various locations of Portugal and Greece. The images are constituted by sticky yellow trap photos and by McPhail trap photos with olive fly exemplars. The performance of the models was evaluated using precision, recall, and mAP.95. The YOLOV7-tiny model best performance is 88.3% of precision, 85% of Recall, 90% of mAP.50, and 53% of mAP.95. The YOLOV8n model best performance is 85% of precision, 85% of Recall, 90% mAP.50, and 55% of mAP.50 YOLO8n model achieved worst results than YOLOv7-tiny for a dataset without negative images (images without olive fly exemplars). Aiming at installing an experimental prototype in the olive grove, the YOLOv8n model was implemented in a Ubuntu Server 23.04 Raspberry PI 3 microcomputer.	pt_PT
dc.description.sponsorship	The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CIMO (UIDB/00690/2020 and UIDP/00690/2020) and SusTEC (LA/P/0007/2020).	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	Alves, Adília; Pereira, José; Khanal, Salik; Morais, A. Jorge; Filipe, Vitor (2024). Pest Detection in Olive Groves Using YOLOv7 and YOLOv8 Models. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 2, p. 50–62. ISBN 978-3-031-53035-7	pt_PT
dc.identifier.doi	10.1007/978-3-031-53036-4_4	pt_PT
dc.identifier.isbn	978-3-031-53035-7
dc.identifier.isbn	978-3-031-53036-4
dc.identifier.uri	http://hdl.handle.net/10198/30394
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.relation	Mountain Research Center
dc.relation	Mountain Research Center
dc.relation	Associate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Olives sustainable production	pt_PT
dc.subject	Convolutional Neural Network	pt_PT
dc.subject	Deep Learning	pt_PT
dc.subject	YOLOv7	pt_PT
dc.subject	YOLOv8	pt_PT
dc.title	Pest Detection in Olive Groves Using YOLOv7 and YOLOv8 Models	pt_PT
dc.type	conference paper
dspace.entity.type	Publication
oaire.awardTitle	Mountain Research Center
oaire.awardTitle	Mountain Research Center
oaire.awardTitle	Associate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardURI	info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00690%2F2020/PT
oaire.awardURI	info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00690%2F2020/PT
oaire.awardURI	info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.citation.endPage	62	pt_PT
oaire.citation.startPage	50	pt_PT
oaire.citation.title	3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023)	pt_PT
oaire.fundingStream	6817 - DCRRNI ID
oaire.fundingStream	6817 - DCRRNI ID
oaire.fundingStream	6817 - DCRRNI ID
person.familyName	Alves
person.familyName	Pereira
person.givenName	Adília
person.givenName	José Alberto
person.identifier.ciencia-id	0019-58CC-96C9
person.identifier.ciencia-id	611F-80B2-A7C1
person.identifier.orcid	0000-0002-3792-1968
person.identifier.orcid	0000-0002-2260-0600
person.identifier.rid	L-6798-2014
person.identifier.scopus-author-id	57204366348
project.funder.identifier	http://doi.org/10.13039/501100001871
project.funder.identifier	http://doi.org/10.13039/501100001871
project.funder.identifier	http://doi.org/10.13039/501100001871
project.funder.name	Fundação para a Ciência e a Tecnologia
project.funder.name	Fundação para a Ciência e a Tecnologia
project.funder.name	Fundação para a Ciência e a Tecnologia
rcaap.rights	restrictedAccess	pt_PT
rcaap.type	conferenceObject	pt_PT
relation.isAuthorOfPublication	f85c13a5-8370-4647-971d-00b44123739c
relation.isAuthorOfPublication	7932162e-a2da-4913-b00d-17babbe51857
relation.isAuthorOfPublication.latestForDiscovery	f85c13a5-8370-4647-971d-00b44123739c
relation.isProjectOfPublication	29718e93-4989-42bb-bcbc-4daff3870b25
relation.isProjectOfPublication	0aac8939-28c2-46f4-ab6b-439dba7f9942
relation.isProjectOfPublication	6255046e-bc79-4b82-8884-8b52074b4384
relation.isProjectOfPublication.latestForDiscovery	0aac8939-28c2-46f4-ab6b-439dba7f9942