000			02900nam a22003257a 4500
003			OSt
005			20240506054546.0
008			240501b ||||| |||| 00| 0 eng d
040			_bEnglish. _cCvSU-CCAT Campus Library. _erda.
050			_aUM TA 1637 _bM88 2019
100			_aMutia, Kim Brian, author. _911846
245			_aMediLeaf : a Herbal Plant Classification System using Image Processing / _cKim Brian Mutia, Keysha R. Pareja, and Carla Angelica A. Maghirang.
260			_aRosario, Cavite : _bCavite State University-CCAT Campus, _c2019.
300			_axiii, 76 leaves : _billustrations ; _c28 cm
500			_aAn Undergraduate Thesis (BSCos) -- Cavite State University-CCAT Campus, 2019.
504			_aIncludes bibliographical references and appendices.
520			_aMUTIA, KIM BRIAN, PAREJA, KEYSHA R., MAGHIRANG, CARLA ANGELICA R. MediLeaf: A Herbal Plant Classification System Using Image Processing. Cavite State University-CCAT, Rosario, Cavite. May 2019. Adviser: Prof. Christopher G. Estonilo. The study was conducted from August 2018 to April 2019 at the Department of Computer Studies of Cavite State University-CCAT. The general objective of the study was to assist users in identifying the type of herbal plant using computer vision. Specifically, the study aimed to: 1) design and develop the system of identifying the type of herbal plant which is capable of importing and capturing leaf image, and identifying the type of imported or captured leaf image; 2) providing the information and benefits of the herbal plant; 3) evaluate the system in order to determine if it complies with the ISO- IE 25010 software evaluation standards; and prepare an implementation plan for the deployment of the system in the Department of Computer Studies of CvSU-CCAT. The system analysis and design of MediLeaf applied the rapid application development or RAD software development model which was composed of requirement analysis, user design (prototype, test, refine), construction, and cutover. The system was evaluated by five (5) IT experts and ten (10) general end-user in the campus. It gained a mean of 4.49 and described as very satisfactory for the system developer respondents. On the other hand, it gained a mean of 4.58 and described as excellent for the end-users.
546			_aIn English text.
650			_aHerb species recognition. _911847
650			_aComputer vision. _95544
650			_aPlant identification. _911848
650			_aImage processing -- Digital techniques. _911802
650			_aComputer graphics. _9298
700			_aPareja, Keysha R., author. _911849
700			_aMaghirang, Carla Angelica A., author. _911850
700			_aEstonilo, Christopher G., adviser. _95822
700			_aNabablit, Karlo Jose E., technical critic. _99160
942			_2lcc _cT/M/D _hTA 1637 M88 2019 _kUM
999			_c3247 _d3247