000			03257nam a2200337 4500
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040			_cCvSU Main Campus Library
041	0		_aeng
082	0	4	_a621.367 _bB31 2022
100			_aBautista, Gwyneth Mae I. _936789 _eauthor
245	1	0	_aDevelopment of a cacao bean classification system using image processing and artificial neural network / _cby Gwyneth Mae I. Bautista and Nobie-Ann L. Quiñones.
260			_aIndang, Cavite : _bCavite State University- Main Campus, _c2022.
300			_axiii, 86 pages : _billustrations ; _c28 cm
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
500			_aDesign Project (Bachelor of Science in Electronics and Communications Engineering) Cavite State University.
504			_aIncludes bibliographical references.
508			_aCollege of Engineering and Information Technology (CEIT)
520	3		_aBAUTISTA, GWYNETH MAE., and QUIÑONES, NOBIE-ANN. Development of a Cacao Bean Classification System Using Image Processing and Artificial Neural Network. Undergraduate Project Design. Bachelor of Science in Electronics Engineering. Cavite State University, Indang, Cavite. December 2018. Adviser: Edwin R.Arboleda, DEng. Using an image processing method and an artificial neural network model, this paper outlines the development of a prototype for identifying the quality of cacao beans based on the color of its interior. The Philippine National Standards for cacao beans were used as the basis for the system's set of guidelines and standards. The prototype was able to automatically determine the classifications of hybrid Trinitario cacao beans cultivated at Dariano Cacao Farm in Silang, which is located in the province of Cavite in the Philippines. The images employed as the data samples of the project were captured using a camera in a controlled setting. The sample images that have been loaded to the system consisted of 13 cacao beans that were placed in the prototype's sample drawer. Thereafter, the photographs are transferred to the system of the prototype, which then crops each of the 13 cacao beans individually and segments the characteristics of each image based on the RGB values. Classification experiments on 260 cross-cut hybrid Trinitario cacao beans using the artificial neural network classifier yielded an overall accuracy of 91.65 percent. The findings indicate that the developed image processing technique and the Artificial Neural Network or ANN-based classifier have the potential to be used as an efficient instrument for the purpose of classifying cacao beans.
541			_cSubmitted to the University Library _d09/01/2022 _eDP-750
650		0	_aImage processing _91174
650		0	_aImage processing software _919368
690			_aBachelor of Science in Electronics and Communications Engineering _94922
700			_aQuiñones, Nobie-Ann L. _eauthor _936790
700			_aArboleda, Edwin R. _eadviser _94719
856			_p80 _yClick here to view the Abstract and Table of Contents _uhttp://library.cvsu.edu.ph/cgi-bin/koha/opac-retrieve-file.pl?id=b918f304347c76a01ff1a212e63baf8a
942			_2ddc _cMAN
999			_c62394 _d62394