000			02938nam a22002777a 4500
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008			220624b ||||| |||| 00| 0 eng d
040			_bEnglish. _cCvSU-CCAT Campus Library. _erda.
050			_aUM TA 1637 _bA43 2019
100			_aAlmario, John Lloyd P., author. _95671
245			_aCoffee Berry Grader and Ripeness Detector / _cJohn Lloyd P. Almario and Aj Mikko C. Pada
260			_aRosario, Cavite : _bCavite State University-CCAT Campus, _c2019
300			_axiv, 69 leaves : _billustrations ; _c28 cm
500			_aUndergraduate Design Project (BSCpE)--Cavite State University-CCAT Campus, 2019.
504			_aIncludes bibliographical references and appendices.
520			_aALMARIO, 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
650			_aImage processing _xDigital techniques. _9527
650			_aComputer vision programming. _95672
650			_aCoffee berry. _95673
700			_aPada, John Lloyd C., author. _95674
700			_aBurton, Ruth Angeli L., adviser. _95675
700			_aDharma, John Michael A., critic. _95216
942			_2lcc _cT/M/D _hTA 1637 _iA43 2019 _kUM
999			_c1778 _d1778