000			03773ntm a2200337 i 4500
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008			260526t20262026my a|||fr|||| 000 0 eng d
020			_aTHE0010530 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFKOM .J35 2026 r Thesis
100	1		_aJalal Uddin Md Akbar, _eauthor.
245	1	0	_aEnhanced yolov8 with attention mechanism for tomato ripeness detection in smart agriculture / _cJalal Uddin MD Akbar
264		1	_aKuantan, Pahang : _bUMPSA, _c2025
264		4	_c© 2025
300			_axvii, 127 pages : _billustrations ; _c30 cm. + _e1 CD-ROM.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Computing
502			_aThesis (Master of Science) -- Universiti Malaysia Pahang - 2026
504			_aIncludes bibliographical references
520	3		_aAccurate 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	2	0	_aFaculty of Computing _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
942			_2lcc _cTHESIS
999			_c104895 _d104901