Detection of Sunn Pests Using Sound Signal Processing Methods

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dc.authorid0000-0002-2954-9430
dc.authorid0000-0002-5145-5991
dc.authorscopusid57191838579
dc.authorscopusid56780324000
dc.authorscopusid56631551100
dc.authorwosidKırcı, Murvet/AAD-5676-2021
dc.authorwosidKıvan, Müjgan/C-3427-2018
dc.contributor.authorYazgaç, Bilgi Görkem
dc.contributor.authorKırcı, Mürvet
dc.contributor.authorKıvan, Müjgan
dc.date.accessioned2022-05-11T14:43:31Z
dc.date.available2022-05-11T14:43:31Z
dc.date.issued2016
dc.departmentFakülteler, Ziraat Fakültesi, Bitki Koruma Bölümü
dc.description5th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) -- JUL 18-20, 2016 -- Inst Agr Resources & Regional Planning, Chinese Acad Agr Sci, Tianjin, PEOPLES R CHINA
dc.description.abstractExtensive consumption of cereals as food in different domestic cousins places great demand the detection of cereal pest and struggle against them. Sunn pests such as Eurygaster integriceps, Eurygaster austriaca, Aelia rostrata and Aelia acuminata are insects with similar seasonal behaviors and dominant threat to the cereal plantations of Turkey. In this work, a microphone which works in acoustic and ultrasonic sound levels with the ability of making recordings with high frequency rate is used. Following the recording of sunn pest sounds with laboratory and outdoor conditions, the sound feature vectors are obtained with the application of different methods such as Linear Predictive Cepstral Coefficients (LPCC), Line Spectral Frequencies (LSF) and Mel Frequency Cepstral Coefficients (MFCC). By analyzing different kNN models it is shown that the automatic detection of sunn pests is possible with sound processing and machine learning methods. The best results is achieved with the overall accuracy of 93.6% using the combination of MFCC and LSF methods.
dc.description.sponsorshipTianjin Polytechn Univ, George Mason Univ, Ctr Spatial Informat Sci & Syst, CSISS Fdn Inc, Open Geospatial Consortium, Turkish Minist Agr, TARBIL Agr Informat Appl Res Ctr, Istanbul Tech Univ, Chinese Acad Agr Sci, Chinese Soc Agr Resources & Regional Planning, Chinese Assoc Agr Sci Soc, Wuhan Univ, IEEE Geoscience & Remote Sensing Soc, State Adm Foreign Experts Affairs China, Minist Agr, Natl Nat Sci Fdn China
dc.identifier.endpage459
dc.identifier.issn2334-3168
dc.identifier.scopus2-s2.0-84994137820
dc.identifier.scopusqualityN/A
dc.identifier.startpage454
dc.identifier.urihttps://hdl.handle.net/20.500.11776/9633
dc.identifier.wosWOS:000391252300090
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKıvan, Müjgan
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectCereals
dc.subjectsunn pests
dc.subjectsound processing
dc.subjectsignal processing
dc.subjectfeature extraction
dc.subjectLPCC
dc.subjectLSF
dc.subjectMFCC
dc.subjectmachine learning
dc.subjectkNN
dc.subjectInsect
dc.subjectClassification
dc.subjectWheat
dc.titleDetection of Sunn Pests Using Sound Signal Processing Methods
dc.typeConference Object

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