Unharvested palm fruit bunch ripeness detection with hybrid color correction
Unharvested palm fruit bunch (PFB) ripeness detection is crucial for efficient palm oil production as labor costs rise and worker shortages grow. Challenges remain under varying illumination, affecting detection performance. Although the deep learning-based color constancy model shows promise, the l...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S277237552400248X |
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| author | Cheng Chang Rajendran Parthiban Vineetha Kalavally Yew Mun Hung Xin Wang |
| author_facet | Cheng Chang Rajendran Parthiban Vineetha Kalavally Yew Mun Hung Xin Wang |
| author_sort | Cheng Chang |
| collection | DOAJ |
| description | Unharvested palm fruit bunch (PFB) ripeness detection is crucial for efficient palm oil production as labor costs rise and worker shortages grow. Challenges remain under varying illumination, affecting detection performance. Although the deep learning-based color constancy model shows promise, the limitation lies in the lack of data. This paper addresses these issues by proposing a hybrid color correction method that combines the learning-based and the physical-based color constancy technique, then trains with YOLOv8 to enhance unharvested PFB detection performance. Specifically, the captured images and their corresponding simultaneous spectra are used to generate the ground truth images, then map the images to other ambient spectra to train the color constancy model, and finally, train the YOLOv8 with the corrected images for ripeness detection. Results demonstrate a 1.5% improvement in the mean Average Precision (mAP) @0.5 of YOLOv8, increasing from 88.2% to 89.7% after applying the proposed hybrid color correction method. For each ripeness category, the mAP@0.5 for unripe and ripe bunches increased by 1.7% and 1.8%, respectively. Underripe bunches exhibit the most increment in mAP of 2.1%, while the overripe bunch gives the least increase of 0.4%. The qualitative results show that the model has enhanced the ability to distinguish subtle differences in PFB ripeness. Consequently, it advances the effectiveness and reliability of PFB ripeness detection in practical palm oil production scenarios, especially for unripe to ripe bunches wherein color gives the most information. This research demonstrates potential in object detection tasks that incorporate illumination-based color correction methods. |
| format | Article |
| id | doaj-art-d1d6395d5ac2472e893a6cd12efdcaa5 |
| institution | Kabale University |
| issn | 2772-3755 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-d1d6395d5ac2472e893a6cd12efdcaa52024-12-13T11:08:10ZengElsevierSmart Agricultural Technology2772-37552024-12-019100643Unharvested palm fruit bunch ripeness detection with hybrid color correctionCheng Chang0Rajendran Parthiban1Vineetha Kalavally2Yew Mun Hung3Xin Wang4Department of Mechanical Engineering, School of Engineering, Monash University Malaysia, Subang Jaya 47500, MalaysiaDepartment of Electrical and Robotics Engineering, Intelligent Lighting Laboratory, School of Engineering, Monash University Malaysia, Subang Jaya 47500, Malaysia; Department of Electrical and Computer Systems Engineering, Faculty of Engineering, Monash University, Clayton 3800, AustraliaDepartment of Electrical and Robotics Engineering, Intelligent Lighting Laboratory, School of Engineering, Monash University Malaysia, Subang Jaya 47500, MalaysiaDepartment of Mechanical Engineering, School of Engineering, Monash University Malaysia, Subang Jaya 47500, MalaysiaDepartment of Mechanical Engineering, School of Engineering, Monash University Malaysia, Subang Jaya 47500, Malaysia; Corresponding author: Xin Wang.Unharvested palm fruit bunch (PFB) ripeness detection is crucial for efficient palm oil production as labor costs rise and worker shortages grow. Challenges remain under varying illumination, affecting detection performance. Although the deep learning-based color constancy model shows promise, the limitation lies in the lack of data. This paper addresses these issues by proposing a hybrid color correction method that combines the learning-based and the physical-based color constancy technique, then trains with YOLOv8 to enhance unharvested PFB detection performance. Specifically, the captured images and their corresponding simultaneous spectra are used to generate the ground truth images, then map the images to other ambient spectra to train the color constancy model, and finally, train the YOLOv8 with the corrected images for ripeness detection. Results demonstrate a 1.5% improvement in the mean Average Precision (mAP) @0.5 of YOLOv8, increasing from 88.2% to 89.7% after applying the proposed hybrid color correction method. For each ripeness category, the mAP@0.5 for unripe and ripe bunches increased by 1.7% and 1.8%, respectively. Underripe bunches exhibit the most increment in mAP of 2.1%, while the overripe bunch gives the least increase of 0.4%. The qualitative results show that the model has enhanced the ability to distinguish subtle differences in PFB ripeness. Consequently, it advances the effectiveness and reliability of PFB ripeness detection in practical palm oil production scenarios, especially for unripe to ripe bunches wherein color gives the most information. This research demonstrates potential in object detection tasks that incorporate illumination-based color correction methods.http://www.sciencedirect.com/science/article/pii/S277237552400248XHybrid color correction, Unharvested palm fruit bunch, Fruit ripeness detection, Deep learning, YOLO |
| spellingShingle | Cheng Chang Rajendran Parthiban Vineetha Kalavally Yew Mun Hung Xin Wang Unharvested palm fruit bunch ripeness detection with hybrid color correction Smart Agricultural Technology Hybrid color correction, Unharvested palm fruit bunch, Fruit ripeness detection, Deep learning, YOLO |
| title | Unharvested palm fruit bunch ripeness detection with hybrid color correction |
| title_full | Unharvested palm fruit bunch ripeness detection with hybrid color correction |
| title_fullStr | Unharvested palm fruit bunch ripeness detection with hybrid color correction |
| title_full_unstemmed | Unharvested palm fruit bunch ripeness detection with hybrid color correction |
| title_short | Unharvested palm fruit bunch ripeness detection with hybrid color correction |
| title_sort | unharvested palm fruit bunch ripeness detection with hybrid color correction |
| topic | Hybrid color correction, Unharvested palm fruit bunch, Fruit ripeness detection, Deep learning, YOLO |
| url | http://www.sciencedirect.com/science/article/pii/S277237552400248X |
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