Classifying Adult Mango Pulp Weevil Activity using Support Vector Machine

被引：1

作者：

Dela Cruz, Jennifer C. ^{[1
]}

Centeno, Juan Carlos F. ^{[1
]}

Faulve, Geovane R., Jr. ^{[1
]}

Pascasio, Gerald John R. ^{[1
]}

Banlawe, Ivane Ann P. ^{[1
]}

机构：

[1] Mapua Univ, Sch Elect Elect & Comp Engn, Muralla St, Manila 1002, Philippines

来源：

2020 IEEE 12TH INTERNATIONAL CONFERENCE ON HUMANOID, NANOTECHNOLOGY, INFORMATION TECHNOLOGY, COMMUNICATION AND CONTROL, ENVIRONMENT, AND MANAGEMENT (HNICEM) | 2020年

关键词：

Mango Pulp Weevil; Adult MPW; feature extraction; SVM; MFCC; MATLAB; COLEOPTERA;

D O I：

10.1109/HNICEM51456.2020.9400041

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

After the discovery of the existence of mango pulp weevil in Palawan the island has been under quarantine for exporting mangoes. Detection of the pest prove to be a difficult task as the pest do not leave a physical sign that a mango has been damaged by the pests. Infested mangoes are wasted as it cannot be sold due to the damages. This study serves as a base study for a non-invasive mango pulp weevil detection by using machine learning and audio feature extraction tools of MATLAB. Audio is recorded using a MEMS microphone and is placed inside a soundproof chamber to minimize the noise. The study was able to achieve a high accuracy on characterizing the adult mango pulp weevil activity by using MFCC as features extraction for identifying its activity.

引用

页数：6

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