MARC details
| 000 -LEADER |
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| fixed length control field |
03773ntm a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20260526100446.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
260526t20262026my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0010530 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMPSA |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FKOM .J35 2026 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Jalal Uddin Md Akbar, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Enhanced yolov8 with attention mechanism for tomato ripeness detection in smart agriculture / |
| Statement of responsibility, etc. |
Jalal Uddin MD Akbar |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2025 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2025 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xvii, 127 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM. |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Faculty of Computing |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Thesis (Master of Science) -- Universiti Malaysia Pahang - 2026 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
Accurate object detection and classification are crucial in precision agriculture for optimizing yield and quality, particularly for determining tomato ripeness stages. However, automated visual assessment in smart agriculture environments faces challenges like variable lighting, occlusions, and subtle ripeness variations. While state-of-the-art detectors like YOLOv8 are fast, their standard architectures can struggle with the fine-grained feature sensitivity needed for accurate ripeness grading and present a trade-off between performance and the computational efficiency required for real-time, edge deployment. This study addresses these limitations by developing and evaluating YOLOv8-ResCBAM, an optimized model tailored for reliable and efficient multi-stage tomato ripeness detection under challenging conditions. The methodology involved curating and preprocessing a comprehensive tomato dataset with extensive augmentation. Key optimizations included strategically reducing layers in the YOLOv8s architecture to create a lightweight backbone and systematically integrating and evaluating several attention mechanisms (CBAM, GAM, ECA, Shuffle Attention, ResCBAM) with this modified architecture. The Residual Convolutional Block Attention Module (ResCBAM) was identified as the optimal mechanism for enhancing focus on subtle ripeness indicators and improving effectiveness in challenging conditions. Performance evaluation confirmed the superiority of YOLOv8- ResCBAM, achieving impressive mAP@0.5 of 91.2%, a significant 6.8% improvement over the baseline YOLOv8s. The model also demonstrated substantial gains in precision (89.2%), recall (86.5%), and F1-score (87.8%). These accuracy improvements are attributed to the synergy between the lightweight design and ResCBAM’s enhanced feature focus, effectively handling occlusion and lighting variations. Critically, this was achieved alongside high computational efficiency (40 FPS, 7.84M parameters, 17.2 MB size), validating its suitability for real-time, resource-constrained deployment. This research contributes a validated, optimized model balancing accuracy and efficiency for tomato ripeness detection, advancing AI-driven crop monitoring for improved operational efficiency and resource management. Future work includes extending the model to other crops, testing in diverse outdoor conditions, and integration with IoT systems. This study provides an effective, optimized deep learning solution, offering insights into tailoring vision models for specific precision agriculture needs and paving the way for more sustainable farming practices. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Faculty of Computing |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Theses |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |