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.
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页数：6
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