Leaf-based plant species recognition based on improved local binary pattern and extreme learning machine

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dc.authoridHanbay, Davut/0000-0003-2271-7865
dc.authorwosidHanbay, Davut/AAG-8511-2019
dc.contributor.authorTurkoglu, Muammer
dc.contributor.authorHanbay, Davut
dc.date.accessioned2024-08-04T20:45:55Z
dc.date.available2024-08-04T20:45:55Z
dc.date.issued2019
dc.departmentİnönü Üniversitesien_US
dc.description.abstractOver the past 15 years, many feature extraction methods have been used and developed for the recognition of plant species. These methods have mostly been performed using separation operations from the background based on a pre-processing stage. However, the Local Binary Patterns (LBP) method, which provides high performance in object recognition, is used to obtain textural features from images without need for a pre-processing stage. In this paper, we propose different approaches based on LBP for the recognition of plant leaves using extracted texture features from plant leaves. While the original LBP converts color images to gray tones, the proposed methods are applied by using the R and G color channel of images. In addition, we evaluate the robustness of the proposed methods against noise such as salt & pepper and Gaussian. Later, the obtained features from the proposed methods were classified and tested using the Extreme Learning Machine (ELM) method. The experimental works were performed using various plant leaf datasets such as Flavia, Swedish, ICL, and Foliage. According to the obtained performance results, the calculated accuracy values for Flavia, Swedish, ICL and Foliage datasets were 98.94%, 99.46%, 83.71%, and 92.92%, respectively. The results demonstrate that the proposed method was more successful when compared to the original LBP, improved LBP methods, and other image descriptors for both noisy and noiseless images. (C) 2019 Elsevier B.V. All rights reserveden_US
dc.identifier.doi10.1016/j.physa.2019.121297
dc.identifier.issn0378-4371
dc.identifier.issn1873-2119
dc.identifier.scopus2-s2.0-85065520212en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1016/j.physa.2019.121297
dc.identifier.urihttps://hdl.handle.net/11616/98787
dc.identifier.volume527en_US
dc.identifier.wosWOS:000480625700128en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofPhysica A-Statistical Mechanics and Its Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPlant classificationen_US
dc.subjectLocal binary patternen_US
dc.subjectImage descriptorsen_US
dc.subjectRegion-overall LBPen_US
dc.subjectExtreme learning machineen_US
dc.titleLeaf-based plant species recognition based on improved local binary pattern and extreme learning machineen_US
dc.typeArticleen_US

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