Date
5-2010
Degree
Bachelor of Science in Agricultural Engineering
Major Course
Major in Agricultural Power and Machinery Engineering
College
College of Engineering and Agro-Industrial Technology (CEAT)
Adviser/Committee Chair
Marvin C. Petingco
Committee Member
Rossana Marie C. Amongo, Delfin C. Suministrado
Abstract
A machine vision system, composed of a digital video camera recorder, a light chamber, a Pentium 4 personal computer at 2.66GHz, and named Banana Color Grader was designed for color grading Cavendish banana clusters.The test involved two parts: precision and accuracy. In the test for precision of the software in determining the number of banana pixels at 24 different loading orientation angles, 20 Lakatan banana fingers were used. In the test for accuracy of the software, 153 Cavendish banana clusters were used for color index determination and 60 Cavendish banana clusters were used for the determination of acceptability for export. Results showed that the developed software has 98.88 percent precision in determining the amount of banana pixels in any loading orientation angle. On the other hand, the overall percent accuracy for color index determination was 85%. Lastly, in determining the acceptability of banana clusters for export, the software has 93.33% accuracy.
Language
English
LC Subject
Banana--musa.
Call Number
LG 993.5 2010 A2 B85
Recommended Citation
Buladaco, Jan Erwin Suarez, "Color grading of Cavendish banana (Musa acuminata Colla cv. 'Gran Nain') clusters using machine vision." (2010). Undergraduate Theses. 216.
https://www.ukdr.uplb.edu.ph/etd-undergrad/216
Document Type
Thesis