MediLeaf : a Herbal Plant Classification System using Image Processing / Kim Brian Mutia, Keysha R. Pareja, and Carla Angelica A. Maghirang.
Material type:
TextPublisher: Rosario, Cavite : Cavite State University-CCAT Campus, 2019Description: xiii, 76 leaves : illustrations ; 28 cmSubject(s): Herb species recognition | Computer vision | Plant identification | Image processing -- Digital techniques | Computer graphicsLOC classification: UM TA 1637 | M88 2019Summary: MUTIA, 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.
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Cavite State University - CCAT Campus | Thesis/Manuscript/Dissertation | TH | UM TA 1637 M88 2019 (Browse shelf) | 1 | Available | T0005216 |
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| UM TA 1637 A43 2019 Coffee Berry Grader and Ripeness Detector / | UM TA 1637 C66 2019 Real Time Obstacle Detection for Visual Impaired Individuals / | UM TA 1637 G83 2018 Development of PEREA: A Raspberry Pi-Based Personal Reading Assistant for the Blind Persons / | UM TA 1637 M88 2019 MediLeaf : a Herbal Plant Classification System using Image Processing / | UM TA165 D45 2018 Development of Electronics Mousetrap / | UM TA 165 D56 2016 The development of automated dissolved oxygen monitoring system / | UM TA 1650 Al43 2019 Stenotype characters translator using computer vision technology / |
An Undergraduate Thesis (BSCos) -- Cavite State University-CCAT Campus, 2019.
Includes bibliographical references and appendices.
MUTIA, 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.
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