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Two-stage automatic diagnosis of Flavescence Dorée based on proximal imaging and artificial intelligence: a multi-year and multi-variety experimental study

dc.rights.licenseopenen_US
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
dc.contributor.authorTARDIF, Malo
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
dc.contributor.authorAMRI, Ahmed
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
dc.contributor.authorKERESZTES, Barna
IDREF: 143373161
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
dc.contributor.authorDESHAYES, Aymeric
dc.contributor.authorMARTIN, Damian
dc.contributor.authorGREVEN, Marc
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
dc.contributor.authorDA COSTA, Jean-Pierre
ORCID: 0000-0003-2390-0047
IDREF: 060561459
dc.date.accessioned2023-03-21T08:40:57Z
dc.date.available2023-03-21T08:40:57Z
dc.date.issued2022-09-19
dc.identifier.issn2494-1271en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/172387
dc.description.abstractEn“Flavescence dorée” (FD) is a grape vine disease caused by the bacterial agent “Candidatus Phytoplasma vitis” and spread by the leafhopper Scaphoideus titanus Ball (Hemiptera: Cicadellidae). The disease is very closely monitored in Europe, as it reduces vine productivity and causes vine death and is also highly transmissible. Currently, the control method used against this disease is a two-pronged approach: i) the spraying of insecticide on a regular basis to kill the vector, and ii) a survey of each row in a vineyard by experts in this disease. Unfortunately, these experts are not able to carry out such a task every year on every vineyard and need an aid for planning their survey.In this study, we propose and evaluate an original automatic method for the detection of FD based on computer vision and artificial intelligence algorithms applied to images acquired by proximal sensing. A two-step approach was used, mimicking an expert’s scouting in the vine rows: (i) the three known isolated symptoms (red or yellow leaves depending on variety, together with a lack of shoot lignification and the presence of desiccated bunches) were detected, (ii) isolated detections were combined to make a diagnosis at image scale; i.e., vine scale. A detection network was used to detect and classify non-healthy leaves into three classes: ‘FD symptomatic leaf', 'Esca leaf' and 'Confounding leaf'; while a segmentation network was used for the retrieval of FD symptomatic shoots and bunches. Finally, the association of detected symptoms was performed by a RandomForest classifier for diagnosis at the image scale. The experimental evaluation was conducted on more than 1000 images collected from 14 blocks planted with five different grape varieties. The detection of the isolated symptoms achieved a precision of between 0.67 and 0.82 and a recall of between 0.39 and 0.59. The classification at the image scale obtained very good results when applied to images acquired under the same conditions, with the same grape varieties as the training images (precision and recall of more than 0.89). The results of the tests on the other grape varieties show the importance of having some of them in the training base in these AI-based approaches.
dc.description.sponsorshipProspect FD : développement d'un outil d'aide à la décision pour la prospection de la flavescence dorée en vigne - ANR-19-ECOM-0004en_US
dc.language.isoENen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subjectFlavescence dorée
dc.subjectComputer vision
dc.subjectArtificial intelligence
dc.subjectProximal sensing
dc.subjectVine disease
dc.subjectImage processing
dc.subjectDeep learning
dc.title.enTwo-stage automatic diagnosis of Flavescence Dorée based on proximal imaging and artificial intelligence: a multi-year and multi-variety experimental study
dc.typeArticle de revueen_US
dc.identifier.doi10.20870/oeno-one.2022.56.3.5460en_US
dc.subject.halSciences de l'ingénieur [physics]en_US
bordeaux.journalOENO Oneen_US
bordeaux.page371-384en_US
bordeaux.volume56en_US
bordeaux.hal.laboratoriesIMS : Laboratoire de l'Intégration du Matériau au Système - UMR 5218en_US
bordeaux.issue3en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionBordeaux INPen_US
bordeaux.institutionCNRSen_US
bordeaux.teamSIGNAL-MOTIVEen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-04038722
hal.version1
hal.date.transferred2023-03-21T08:41:00Z
hal.exporttrue
dc.rights.ccCC BYen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=OENO%20One&rft.date=2022-09-19&rft.volume=56&rft.issue=3&rft.spage=371-384&rft.epage=371-384&rft.eissn=2494-1271&rft.issn=2494-1271&rft.au=TARDIF,%20Malo&AMRI,%20Ahmed&KERESZTES,%20Barna&DESHAYES,%20Aymeric&MARTIN,%20Damian&rft.genre=article


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