Image Processing Techniques based Feature Extraction for Insect Damage Areas

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dc.authorscopusid57694983400en_US
dc.authorscopusid36460530900en_US
dc.contributor.authorAlkan, E.
dc.contributor.authorAydın, A.
dc.date.accessioned2024-08-23T16:07:24Z
dc.date.available2024-08-23T16:07:24Z
dc.date.issued2023en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractMonitoring of forests is important for the diagnosis of insect damage to vegetation. Detection and monitoring of damaged areas facilitates the control of pests for practitioners. For this purpose, Unmanned Aerial Vehicles (UAVs) have been recently used to detect damaged areas. In order to distinguish damage areas from healthy areas on UAV images, it is necessary to extract the feature parameters of the images. Therefore, feature extraction is an important step in Computer Aided Diagnosis of insect damage monitored with UAV images. By reducing the size of the UAV image data, it is possible to distinguish between damaged and healthy areas from the extracted features. The accuracy of the classification algorithm depends on the segmentation method and the extracted features. The Grey-Level Co-occurrence Matrix (GLCM) characterizes areas texture based on the number of pixel pairs with specific intensity values arranged in specific spatial relationships. In this paper, texture characteristics of insect damage areas were extracted from UAV images using with GLCM. The 3000*4000 resolution UAV images containing damaged and healthy larch trees were analyzed using Definiens Developer (e-Cognition software) for multiresolution segmentation to detect the damaged areas. In this analysis, scale parameters were applied as 500, shape 0.1, color 0.9 and compactness 0.5. As a result of segmentation, GLCM homogeneity, GLCM contrast and GLCM entropy texture parameters were calculated for each segment. When calculating the texturing parameters, neighborhoods in different angular directions (0,45,90,135) are taken into account. As a result of the calculations made by considering all directions, it was found that GLCM homogeneity values ranged between 0.08 - 0.2, GLCM contrast values ranged between 82.86 - 303.58 and GLCM entropy values ranged between 7.81 - 8.51. On the other hand, GLCM homogeneity for healthy areas varies between 0.05 - 0.08, GLCM contrast between 441.70 - 888.80 and GLCM entropy between 8.93 - 9.40. The study demonstrated that GLCM technique can be a reliable method to detection of insect damage areas from UAV imagery. © Copyright 2023 by Forest Engineering and Technologies.en_US
dc.identifier.doi10.33904/ejfe.1320121
dc.identifier.endpage40en_US
dc.identifier.issn2149-5637
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85168364671en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage34en_US
dc.identifier.trdizinid1185435en_US
dc.identifier.urihttps://doi.org/10.33904/ejfe.1320121
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1185435
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14643
dc.identifier.volume9en_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherForest Engineering and Technologies Platformen_US
dc.relation.ispartofEuropean Journal of Forest Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectGray level co-occurrence matrixen_US
dc.subjectImage processingen_US
dc.subjectInsect damageen_US
dc.titleImage Processing Techniques based Feature Extraction for Insect Damage Areasen_US
dc.typeArticleen_US

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