Pid based green coffee quality sorter / by Abel James N. Lualhati and Jhamil B. Mariano.

By:

Lualhati, Abel James N [author]

Contributor(s):

Material type: Text

TextLanguage: English Publication details: Indang, Cavite : Cavite State University- Main Campus, 2019.Description: xiv, 87 pages : illustrations ; 28 cmContent type:

text

Media type:

unmediated

Carrier type:

volume

Subject(s):

DDC classification:

621.392 L96 2019

Online resources:

Click here to view thesis abstract and table of contents

Production credits:

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

Abstract: LUALHATI, ABEL JAMES N. and MARIANO, JHAMIL B., PM Based Green COffee Quality Sorter. Undergraduate Thesis. Bachelor of Science in Computer Engineering. Cavite State University, Indang, Cavite. June, 2019. Adviser: Ms. Sheryl D. Fenol. The study was conducted to design a PID based green coffee quality sorter for the farmers and researchers. The project aimed to help the coffee farmers sort and increase the quality of their output in a more effective way. The general objective of the study was to design and develop a PID based green coffee quality sorter. The study specifically aimed to design and construct the algorithm for the controller circuit for the system; design and fabricate the PID based green coffee quality sorter; develop a neural network for the system; and test and evaluate the system through preliminary testing. The materials used in the study were: microcontroller unit, stepper motor, servo motor, switching power supply, acrylic glass, aluminum framework, and USB webcams. The PID based green coffee quality sorter was able to separate defective coffee beans from the good green coffee bean arranged in a linear manner using neural network and image processing. Two (2) webcams were used to take images of both sides of the bean. The quality of the coffee bean was determined by the prediction test of both sides. The device was found to be functional in terms of accuracy with a score of 85%. It can sort 1 kilogram of beans within 2 hours and 45 minutes. The study met its objectives and in order to be used the device efficiently, the speed must be increased and the dataset of the neural network must be increased. The PID based green coffee quality sorter had a total cost of P26.290.00

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Item type	Current library	Collection	Call number	Materials specified	URL	Status	Notes	Date due	Barcode
Theses / Manuscripts	Ladislao N. Diwa Memorial Library Theses Section	Non-fiction	621.392 L96 2019 (Browse shelf(Opens below))		Link to resource	Room use only	T-8670		00081110

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Thesis (Bachelor of Science in Computer Engineering) Cavite State University.

Includes bibliographical references.

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

LUALHATI, ABEL JAMES N. and MARIANO, JHAMIL B., PM Based Green COffee Quality Sorter. Undergraduate Thesis. Bachelor of Science in Computer Engineering. Cavite State University, Indang, Cavite. June, 2019. Adviser: Ms. Sheryl D. Fenol.
The study was conducted to design a PID based green coffee quality sorter for the farmers and researchers. The project aimed to help the coffee farmers sort and increase the quality of their output in a more effective way. The general objective of the study was to design and develop a PID based green coffee quality sorter. The study specifically aimed to design and construct the algorithm for the controller circuit for the system; design and fabricate the PID based green coffee quality sorter; develop a neural network for the system; and test and evaluate the system through preliminary testing. The materials used in the study were: microcontroller unit, stepper motor, servo motor, switching power supply, acrylic glass, aluminum framework, and USB webcams. The PID based green coffee quality sorter was able to separate defective coffee beans from the good green coffee bean arranged in a linear manner using neural network and image processing. Two (2) webcams were used to take images of both sides of the bean. The quality of the coffee bean was determined by the prediction test of both sides. The device was found to be functional in terms of accuracy with a score of 85%. It can sort 1 kilogram of beans within 2 hours and 45 minutes. The study met its objectives and in order to be used the device efficiently, the speed must be increased and the dataset of the neural network must be increased. The PID based green coffee quality sorter had a total cost of P26.290.00

Submitted to the University Library 01/28/2020 T-8670

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