Almario, John Lloyd P., author.
Coffee Berry Grader and Ripeness Detector / John Lloyd P. Almario and Aj Mikko C. Pada - Rosario, Cavite : Cavite State University-CCAT Campus, 2019 - xiv, 69 leaves : illustrations ; 28 cm
Undergraduate Design Project (BSCpE)--Cavite State University-CCAT Campus, 2019.
Includes bibliographical references and appendices.
ALMARIO, JOHN LLOYD P., PADA, AJ MIKKO C. Coffee Berry Grader and Ripeness Detector. Undergraduate Design Project. Department of Engineering. Cavite State University — Cavite College of Arts and Trades, Rosario, Cavite. June 2019. Adviser: Engr. Ruth Angeli L. Burton. Technical Critic: Engr. John Michael A. Dharma.
The study was conducted from December 2018 to May 2019 to create a Coffee Detector, a scanner for determining the size and level of ripeness of the berry. Specifically aimed to: (1) design and develop a detector for grading coffee berries and determination of degree of ripeness; (2) test the functionality of the prototype according to: scanning speed; scanning capacity;(3) evaluate the device based on ISOMEC 25010 according to: functional stability, performance efficiency, compatibility, usability, _ reliability, security, maintainability, portability, effectiveness, efficiency, satisfaction, freedom from risk and context coverage.
Coffee Berry Detector allows the user to determine the size and level of ripeness of the coffee berry. The developers evaluated the detector in terms of functionality, reliability, usability, efficiency, maintainability and portability. Based on the results of the evaluation the study met its objectives and serve its functions. The security parameter of the evaluation has the highest resulting score of 4.58 grand mean and the lowest was compatibility having 4.30 grand mean. The sensitivity of Pi-camera to light revealed some limitations of the system and arises some recommendations for the improvement of the study. The study was limited for capturing and analyzing the external part of the two variety of coffee berries which are Excelsa and Robusta.
Keywords: Coffee Berry, Pi-camera, Coffee Grader, Ripeness Detector, Color Detector
Image processing--Digital techniques.
Computer vision programming.
Coffee berry.
UM TA 1637 / A43 2019
Coffee Berry Grader and Ripeness Detector / John Lloyd P. Almario and Aj Mikko C. Pada - Rosario, Cavite : Cavite State University-CCAT Campus, 2019 - xiv, 69 leaves : illustrations ; 28 cm
Undergraduate Design Project (BSCpE)--Cavite State University-CCAT Campus, 2019.
Includes bibliographical references and appendices.
ALMARIO, JOHN LLOYD P., PADA, AJ MIKKO C. Coffee Berry Grader and Ripeness Detector. Undergraduate Design Project. Department of Engineering. Cavite State University — Cavite College of Arts and Trades, Rosario, Cavite. June 2019. Adviser: Engr. Ruth Angeli L. Burton. Technical Critic: Engr. John Michael A. Dharma.
The study was conducted from December 2018 to May 2019 to create a Coffee Detector, a scanner for determining the size and level of ripeness of the berry. Specifically aimed to: (1) design and develop a detector for grading coffee berries and determination of degree of ripeness; (2) test the functionality of the prototype according to: scanning speed; scanning capacity;(3) evaluate the device based on ISOMEC 25010 according to: functional stability, performance efficiency, compatibility, usability, _ reliability, security, maintainability, portability, effectiveness, efficiency, satisfaction, freedom from risk and context coverage.
Coffee Berry Detector allows the user to determine the size and level of ripeness of the coffee berry. The developers evaluated the detector in terms of functionality, reliability, usability, efficiency, maintainability and portability. Based on the results of the evaluation the study met its objectives and serve its functions. The security parameter of the evaluation has the highest resulting score of 4.58 grand mean and the lowest was compatibility having 4.30 grand mean. The sensitivity of Pi-camera to light revealed some limitations of the system and arises some recommendations for the improvement of the study. The study was limited for capturing and analyzing the external part of the two variety of coffee berries which are Excelsa and Robusta.
Keywords: Coffee Berry, Pi-camera, Coffee Grader, Ripeness Detector, Color Detector
Image processing--Digital techniques.
Computer vision programming.
Coffee berry.
UM TA 1637 / A43 2019