PlantDiseaseNet: convolutional neural network ensemble for plant disease and pest detection

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dc.authoridYanikoglu, Berrin/0000-0001-7403-7592
dc.authoridHanbay, Davut/0000-0003-2271-7865
dc.authorwosidYanikoglu, Berrin/AAE-4843-2022
dc.authorwosidHanbay, Davut/AAG-8511-2019
dc.contributor.authorTurkoglu, Muammer
dc.contributor.authorYanikoglu, Berrin
dc.contributor.authorHanbay, Davut
dc.date.accessioned2024-08-04T20:50:23Z
dc.date.available2024-08-04T20:50:23Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractPlant diseases and pests cause significant losses in agriculture, with economic, ecological and social implications. Therefore, early detection of plant diseases and pests via automated methods are very important. Recent machine learning-based studies have become popular in the solution of agricultural problems such as plant diseases. In this work, we present two classification models based on deep feature extraction from pre-trained convolutional neural networks. In the proposed models, we fine-tune and combine six state-of-the-art convolutional neural networks and evaluate them on the given problem both individually and as an ensemble. Finally, the performances of different combinations based on the proposed models are calculated using a support vector machine (SVM) classifier. In order to verify the validity of the proposed model, we collected Turkey-PlantDataset, consisting of unconstrained photographs of 15 kinds of disease and pest images observed in Turkey. According to the obtained performance results, the accuracy scores are calculated as 97.56% using the majority voting ensemble model and 96.83% using the early fusion ensemble model. The results demonstrate that the proposed models reach or exceed state-of-the-art results for this problem.en_US
dc.identifier.doi10.1007/s11760-021-01909-2
dc.identifier.endpage309en_US
dc.identifier.issn1863-1703
dc.identifier.issn1863-1711
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85109034310en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage301en_US
dc.identifier.urihttps://doi.org/10.1007/s11760-021-01909-2
dc.identifier.urihttps://hdl.handle.net/11616/100019
dc.identifier.volume16en_US
dc.identifier.wosWOS:000667593200001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofSignal Image and Video Processingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPlant disease and pest systemen_US
dc.subjectDeep featuresen_US
dc.subjectSupport vector machineen_US
dc.subjectCNNen_US
dc.subjectFusion ensembleen_US
dc.titlePlantDiseaseNet: convolutional neural network ensemble for plant disease and pest detectionen_US
dc.typeArticleen_US

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