A multi-division convolutional neural network-based plant identification system

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dc.authoridAslan, Muzaffer/0000-0002-2418-9472
dc.authoridHanbay, Davut/0000-0003-2271-7865
dc.authorwosidAslan, Muzaffer/U-5355-2018
dc.authorwosidHanbay, Davut/AAG-8511-2019
dc.contributor.authorTurkoglu, Muammer
dc.contributor.authorAslan, Muzaffer
dc.contributor.authorAri, Ali
dc.contributor.authorAlcin, Zeynep Mine
dc.contributor.authorHanbay, Davut
dc.date.accessioned2024-08-04T20:50:23Z
dc.date.available2024-08-04T20:50:23Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBackground. Plants have an important place in the life of all living things. Today, there is a risk of extinction for many plant species due to climate change and its environmental impact. Therefore, researchers have conducted various studies with the aim of protecting the diversity of the planet's plant life. Generally, research in this area is aimed at determining plant species and diseases, with works predominantly based on plant images. Advances in deep learning techniques have provided very successful results in this field, and have become widely used in research studies to identify plant species. Methods. In this paper, a Multi-Division Convolutional Neural Network (MD-CNN)-based plant recognition system was developed in order to address an agricultural problem related to the classification of plant species. In the proposed system, we divide plant images into equal nxn-sized pieces, and then deep features are extracted for each piece using a Convolutional Neural Network (CNN). For each part of the obtained deep features, effective features are selected using the Principal Component Analysis (PCA) algorithm. Finally, the obtained effective features are combined and classification conducted using the Support Vector Machine (SVM) method. Results. In order to test the performance of the proposed deep-based system, eight different plant datasets were used: Flavia, Swedish, ICL, Foliage, Folio, Flowerl7, Flower102, and LeafSnap. According to the results of these experimental studies, 100% accuracy scores were achieved for the Flavia, Swedish, and Folio datasets, whilst the ICL, Foliage, Flower17, Flower102, and LeafSnap datasets achieved results of 99.77%, 99.93%, 97.87%, 98.03%, and 94.38%, respectively.en_US
dc.identifier.doi10.7717/peerj-cs.572
dc.identifier.issn2376-5992
dc.identifier.pmid34141894en_US
dc.identifier.scopus2-s2.0-85108877532en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.7717/peerj-cs.572
dc.identifier.urihttps://hdl.handle.net/11616/100008
dc.identifier.wosWOS:000658891600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPeerj Incen_US
dc.relation.ispartofPeerj Computer Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPlant Identification Systemen_US
dc.subjectDeep featuresen_US
dc.subjectSupport Vector Machineen_US
dc.subjectPrincipal component analysisen_US
dc.subjectDivision processen_US
dc.titleA multi-division convolutional neural network-based plant identification systemen_US
dc.typeArticleen_US

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