Design and development of coconut maturity detector using acoustic sensing / by Ricka Kaye Anne A. Abrantes and Jhoanie Marie P. Cauan.

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Indang, Cavite : Cavite State University- Main Campus, 2019.Description: xx, 122 pages : illustrations ; cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
Subject(s): DDC classification:
  • 620.0042  Ab8 2019
Online resources: Production credits:
  • College of Engineering and Information Technology (CEIT), Department of Computer and Electronics Engineering
Abstract: ABRANTES, RICKA KAYE ANNE A. and CAUAN, JHOANIE MARIE P., Design and Development of Coconut Maturity Detector Using Acoustic Sensing. Undergraduate Design Project. Bachelor of Science in Electronics and Communications Engineering. Cavite State University, Indang, Cavite. June 2019. Adviser: Nemilyn A. Fadchar. Coconuts vary in flavor and use depending on their age. There are three distinct stages of coconut maturity namely, Mucus-like (Malauhog), Cooked rice-like (Malakanin), and Leather-like (Malakatad). Generally, the objective of the study was to design and develop a coconut maturity detector using acoustic sensing. Specifically, the study aimed to design and interface the circuits for coconut maturity detector, assemble and fabricate the coconut maturity detector device, develop an algorithm for program of the system using microcontroller unit, test and evaluate the device, and conduct cost computation. In this paper, an efficient procedure for maturity level detection of young coconuts was presented. A nondestructive method was used based on the vibration response to determine the internal quality of coconut. The materials that composed the equipment were power source, voltage booster, vibration motor, and vibration sensor. The total cost of the device was Php 2,055.00. The responses of samples to vibration excitation generated by the vibration motor were read by the vibration sensor. Vibration data was collected from 100 coconuts of three categories, where 75 percent complied training data set and 25 percent consisted the evaluation. Vibration signals were transformed from time-domain to frequency-domain. Artificial Neural Network (ANN) analysis was applied as a classifier in decision-making stage. Upon ANN classification, the experimental results showed that the Ivialauhog ranged from 111 Hz to 531 Hz, Malakanin had frequencies greater than 531 Hz but less than or equal to 883 Hz, and IvIalakatad ranged greater than 883 Hz and less than or equal to 1337 Hz. The device analyzed the vibration response of the coconut on which range it belongs. On no more than 20 seconds, the device displayed its output: the type of coconut and the frequency of the response. 22 out of 25 coconuts were classified correctly by the device, while 21 out of 25 coconuts were identified rightly by the coconut vendor. The device successfully read the frequencies of the response signals as well as the type of coconut. Results proved that the device is more precise than the coconut vendors. This method allowed identification at a 90 percent level of precision. Hence, the proposed method can reliably detect coconut maturity level.
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Theses / Manuscripts Theses / Manuscripts Ladislao N. Diwa Memorial Library Theses Section Non-fiction 620.0042 Ab8 2019 (Browse shelf(Opens below)) Link to resource Room use only DP-694 00079500

Design Project (Bachelor of Science in Electronics and Communication Engineering) Cavite State University.

Includes bibliographical references.

College of Engineering and Information Technology (CEIT), Department of Computer and Electronics Engineering

ABRANTES, RICKA KAYE ANNE A. and CAUAN, JHOANIE MARIE P., Design and Development of Coconut Maturity Detector Using Acoustic Sensing. Undergraduate Design Project. Bachelor of Science in Electronics and Communications Engineering. Cavite State University, Indang, Cavite. June 2019. Adviser: Nemilyn A. Fadchar.
Coconuts vary in flavor and use depending on their age. There are three distinct stages of coconut maturity namely, Mucus-like (Malauhog), Cooked rice-like (Malakanin), and Leather-like (Malakatad). Generally, the objective of the study was to design and develop a coconut maturity detector using acoustic sensing. Specifically, the study aimed to design and interface the circuits for coconut maturity detector, assemble and fabricate the coconut maturity detector device, develop an algorithm for program of the system using microcontroller unit, test and evaluate the device, and conduct cost computation. In this paper, an efficient procedure for maturity level detection of young coconuts was presented. A nondestructive method was used based on the vibration response to determine the internal quality of coconut. The materials that composed the equipment were power source, voltage booster, vibration motor, and vibration sensor. The total cost of the device was Php 2,055.00. The responses of samples to vibration excitation generated by the vibration motor were read by the vibration sensor. Vibration data was collected from 100 coconuts of three categories, where 75 percent complied training data set and 25 percent consisted the evaluation. Vibration signals were transformed from time-domain to frequency-domain. Artificial Neural Network (ANN) analysis was applied as a classifier in decision-making stage. Upon ANN classification, the experimental results showed that the Ivialauhog ranged from 111 Hz to 531 Hz, Malakanin had frequencies greater than 531 Hz but less than or equal to 883 Hz, and IvIalakatad ranged greater than 883 Hz and less than or equal to 1337 Hz. The device analyzed the vibration response of the coconut on which range it belongs. On no more than 20 seconds, the device displayed its output: the type of coconut and the frequency of the response. 22 out of 25 coconuts were classified correctly by the device, while 21 out of 25 coconuts were identified rightly by the coconut vendor. The device successfully read the frequencies of the response signals as well as the type of coconut. Results proved that the device is more precise than the coconut vendors. This method allowed identification at a 90 percent level of precision. Hence, the proposed method can reliably detect coconut maturity level.

Submitted to the University Library 02/05/2020 DP-694

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