| 000 -LEADER |
|---|
| fixed length control field |
02234nam a22001817a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
ft8936 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20260112132216.0 |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
QA76.87 R63 2025 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Rodrigo, Kayne Uriel L.; Marcial, Jerriane Hillary Heart S. |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
An enhancement of convolutional neural network algorithm applied in rice leaf disease classification mobile application |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
c2025 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Other physical details |
Undergraduate Thesis: (Bachelor of Science in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2025 |
| 336 ## - CONTENT TYPE |
|---|
| Source |
text |
| Content type term |
text |
| Content type code |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
unmediated |
| Media type term |
unmediated |
| Media type code |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
volume |
| Carrier type term |
volume |
| Carrier type code |
volume |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Statement of responsibility |
ABSTRACT: This study focuses on enhancing a rice leaf disease image classification algorithm to address its limitations, such as inconsistent image segmentation processes, lack of global context understanding, parameter-heavy computer vision models, and unoptimized hyperparameter values. Hence, the researchers provided three contributions. First, the researchers integrated a U2-Net salient object detection algorithm for an intelligent image background removal process into the rice leaf disease dataset. It reaches a consistent object extraction and reaches an optimal level of mean IOU of 86%, a 3% increase compared to the traditional method. Second, the researchers integrated changes to the architecture of the traditional convolutional neural network algorithm by implementing MobileViTV2_050. It offers a lightweight hybrid CNN + Vision Transformer architecture. This resulted in a 6% increase in Top-1 Accuracy from a baseline of 81%, reduced 13.1 million parameters from the traditional algorithm, and having an output model that is significantly 163.62 MB lesser from a baseline of 168 MB. This makes it susceptible to a lightweight level, suitable for mobile devices. It can also accept pre-trained weights, which can potentially increase its accuracy up to an overall 99% accuracy. Lastly, the researchers also tested GridSearchCV with 3-fold cross-validation, which is tested on both traditional CNN and the proposed MobileViTv2_050, resulting in a 2% and 3% F1-Score increase. Overall, the enhanced algorithm became applicable for low-end devices to accommo |
| 942 ## - ADDED ENTRY ELEMENTS |
|---|
| Source of classification or shelving scheme |
|
| Item type |
Thesis/Dissertation |