Abstract
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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