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02551nam a22002417a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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ft8844 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251205084829.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
251127b ||||| |||| 00| 0 eng d |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
T58.6 S27 2025 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
. |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Sarreal, Stanley J.; Surigao, John Dave A.; Torres, Jon Kirby N. |
| 245 ## - TITLE STATEMENT |
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| Title |
Development of a mobile application for disease detection in oregano leaves using ylov8 model, tensorflow, and opencv library |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
. |
| Name of producer, publisher, distributor, manufacturer |
. |
| Date of production, publication, distribution, manufacture, or copyright notice |
c2025 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Other physical details |
Capstone Project: (Bachelor of Science in Information Technology) - Pamantasan ng Lungsod ng Maynila, 2025 |
| 336 ## - CONTENT TYPE |
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| Source |
text |
| Content type term |
text |
| Content type code |
text |
| 337 ## - MEDIA TYPE |
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unmediated |
| Media type term |
unmediated |
| Media type code |
unmediated |
| 338 ## - CARRIER TYPE |
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volume |
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volume |
| Carrier type code |
volume |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
ABSTRACT: Oregano, valued for its culinary and medicinal properties, is susceptible to stress-induced diseases that affect its health and yield. Detecting these diseases is challenging due to the variations in image quality and the need for accurate classification. To address this, advancements in image processing and machine learning provide a promising solution for improving disease detection for Oregano plants. This study explores the development of a mobile application that detects oregano leaf diseases using the YOLOv8 model for accurate classification. It also examines how image quality can be enhanced using OpenCV preprocessing technique to improve detection accuracy. Additionally, the study investigates how the application can provide disease information, including causes, symptoms, and recommended treatments, using TensorFlow. By integrating these technologies, the application aims to improve plant healthy through proper diagnosis and timely intervention. To evaluate the effectiveness of this approach, the study assessed the performance of the model and image enhancement techniques. The YOLOv8 model achieved 90.48% accuracy, with a precision of 80.61%, recall of 76%, and F1 score of 86.30%, highlighting reliable disease identification despite some false negatives. Image enhancement techniques using OpenCV were evaluated using MSE, PSNR, and SSIM, yielding 0.26, 27.13, and 0.93, respectively, indicating effective image quality preservation. Although limitations in handling false negatives and maintaining image clarity under challenging conditions were noted, the findings demonstrate the potential of the proposed application to enhance plant disease detection and support sustainable agriculture. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
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| Classification |
Filipiniana |
| 655 ## - INDEX TERM--GENRE/FORM |
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| Genre/form data or focus term |
academic writing |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Source of classification or shelving scheme |
|
| Item type |
Thesis/Dissertation |
| Koha issues (borrowed), all copies |
1 |