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Testing early detection of pine processionary moth Thaumetopoea pityocampa nests using UAV-based methods

hal.structure.identifierUniversidade de Lisboa = University of Lisbon = Université de Lisbonne [ULISBOA]
dc.contributor.authorGARCIA, André
hal.structure.identifierTelespazio
dc.contributor.authorSAMALENS, Jean-Charles
hal.structure.identifierTelespazio
dc.contributor.authorGRILLET, Arnaud
hal.structure.identifierUniversidade de Lisboa = University of Lisbon = Université de Lisbonne [ULISBOA]
dc.contributor.authorSOARES, Paula
hal.structure.identifierUniversidade de Lisboa = University of Lisbon = Université de Lisbonne [ULISBOA]
dc.contributor.authorBRANCO, Manuela
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorVAN HALDER, Inge
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorJACTEL, Hervé
hal.structure.identifierUniversità degli Studi di Padova = University of Padua [Unipd]
dc.contributor.authorBATTISTI, Andrea
dc.date.issued2023-05-18
dc.identifier.issn1619-0033
dc.description.abstractEnEarly detection of insect infestation is a key to the adoption of control measures appropriated to each local condition. The use of remote sensing was recommended for a quick scanning of large areas, although it does not work well with signals bearing low intensity or items that are difficult to detect. Unmanned Aerial Vehicle (UAV, or drone) may help in getting closer to individual trees and detect atypical signals of small dimensions. The larvae of the pine processionary moth (PPM, Thaumetopoea pityocampa (Denis & Schiffermüller, 1775, Lepidoptera, Notodontidae) build conspicuous silk nests on the external parts of the host plants at the beginning of the winter and their early detection may prompt managers to adopt management techniques. This work aims at testing two deep learning methods (Region-based Convolutional Neural Network - R-CNN and You Only Look Once - YOLO) to detect the nests under three different conditions of host plant species and forest stands in southern Europe. YOLO algorithm provided better results and it allowed us to achieve F1-scores as high as 0.826 and 0.696 for the detection of presence / absence and the individual nests, respectively. The detection of all the nests that can be present on a tree is not achievable with either UAV scanning or traditional ground observation, therefore the integration of the methods may allow the complete efficiency of the surveillance. The use of UAV combined with Artificial Intelligence (AI) image analysis is recommended for further use in forest and urban settings for the detection of the PPM nests. The recommended methods can be extended to other pest systems, especially when specific symptoms can be associated with an insect pest species.
dc.language.isoen
dc.publisherPensoft Publishers
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.enAI algorithm
dc.subject.enforest
dc.subject.enInsecta
dc.subject.enLepidoptera
dc.subject.enNotodontidae
dc.subject.enpest
dc.subject.enPPM
dc.subject.enUAV
dc.subject.enurban
dc.title.enTesting early detection of pine processionary moth Thaumetopoea pityocampa nests using UAV-based methods
dc.typeArticle de revue
dc.identifier.doi10.3897/neobiota.84.95692
dc.subject.halSciences de l'environnement
bordeaux.journalNeoBiota
bordeaux.page267-279
bordeaux.volume84
bordeaux.peerReviewedoui
hal.identifierhal-04513505
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04513505v1
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