Advanced segmentation models for automated capsicum peduncle detection in night-time greenhouse environments
This research addresses challenges in capsicum peduncle detection in night-time greenhouse environments, including low light, uneven illumination, and shadows, using advanced computer vision models. A dataset of 200 images was curated, capturing diverse distances, heights, occlusion levels, and ligh...
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Taylor & Francis Group
2024-12-01
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| Series: | Systems Science & Control Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2024.2437162 |
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| author | Ayan Paul Rajendra Machavaram |
| author_facet | Ayan Paul Rajendra Machavaram |
| author_sort | Ayan Paul |
| collection | DOAJ |
| description | This research addresses challenges in capsicum peduncle detection in night-time greenhouse environments, including low light, uneven illumination, and shadows, using advanced computer vision models. A dataset of 200 images was curated, capturing diverse distances, heights, occlusion levels, and lighting conditions, and was rigorously pre-processed and augmented. Two YOLOv9 instance segmentation variants, YOLOv9c-seg and YOLOv9e-seg, were custom-trained and fine-tuned using Google Colaboratory. YOLOv9c-seg (56.3 MB) achieved superior mean Average Precision (mAP) scores of 0.751 (box) and 0.725 (mask), outperforming YOLOv9e-seg (121.9 MB) with mAP scores of 0.674 (box) and 0.658 (mask). Grounded SAM, a zero-shot segmentation model, achieved maximum peduncle detection confidences of 59% and 49% with positional prompts. Comparative testing on 50 images containing 70 capsicums showed YOLOv9c-seg achieving mean precision, recall, and F1-scores of 0.93, 0.86, and 0.89, respectively, outperforming Grounded SAM (0.86, 0.70, and 0.77). This study highlights the efficacy of single-shot versus zero-shot segmentation models for automated capsicum peduncle detection in controlled agricultural environments, offering insights into model performance and future research directions for model optimization and dataset expansion. |
| format | Article |
| id | doaj-art-17e6e23069ba4c3eb352f4c5730c2da9 |
| institution | OA Journals |
| issn | 2164-2583 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj-art-17e6e23069ba4c3eb352f4c5730c2da92025-08-20T02:36:39ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832024-12-0112110.1080/21642583.2024.2437162Advanced segmentation models for automated capsicum peduncle detection in night-time greenhouse environmentsAyan Paul0Rajendra Machavaram1Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, IndiaIndian Institute of Technology Kharagpur, Kharagpur, West Bengal, IndiaThis research addresses challenges in capsicum peduncle detection in night-time greenhouse environments, including low light, uneven illumination, and shadows, using advanced computer vision models. A dataset of 200 images was curated, capturing diverse distances, heights, occlusion levels, and lighting conditions, and was rigorously pre-processed and augmented. Two YOLOv9 instance segmentation variants, YOLOv9c-seg and YOLOv9e-seg, were custom-trained and fine-tuned using Google Colaboratory. YOLOv9c-seg (56.3 MB) achieved superior mean Average Precision (mAP) scores of 0.751 (box) and 0.725 (mask), outperforming YOLOv9e-seg (121.9 MB) with mAP scores of 0.674 (box) and 0.658 (mask). Grounded SAM, a zero-shot segmentation model, achieved maximum peduncle detection confidences of 59% and 49% with positional prompts. Comparative testing on 50 images containing 70 capsicums showed YOLOv9c-seg achieving mean precision, recall, and F1-scores of 0.93, 0.86, and 0.89, respectively, outperforming Grounded SAM (0.86, 0.70, and 0.77). This study highlights the efficacy of single-shot versus zero-shot segmentation models for automated capsicum peduncle detection in controlled agricultural environments, offering insights into model performance and future research directions for model optimization and dataset expansion.https://www.tandfonline.com/doi/10.1080/21642583.2024.2437162Capsicum peduncle detectionnight-time greenhouseYOLOv9 instance segmentationzero-shot segmentationperformance evaluation |
| spellingShingle | Ayan Paul Rajendra Machavaram Advanced segmentation models for automated capsicum peduncle detection in night-time greenhouse environments Systems Science & Control Engineering Capsicum peduncle detection night-time greenhouse YOLOv9 instance segmentation zero-shot segmentation performance evaluation |
| title | Advanced segmentation models for automated capsicum peduncle detection in night-time greenhouse environments |
| title_full | Advanced segmentation models for automated capsicum peduncle detection in night-time greenhouse environments |
| title_fullStr | Advanced segmentation models for automated capsicum peduncle detection in night-time greenhouse environments |
| title_full_unstemmed | Advanced segmentation models for automated capsicum peduncle detection in night-time greenhouse environments |
| title_short | Advanced segmentation models for automated capsicum peduncle detection in night-time greenhouse environments |
| title_sort | advanced segmentation models for automated capsicum peduncle detection in night time greenhouse environments |
| topic | Capsicum peduncle detection night-time greenhouse YOLOv9 instance segmentation zero-shot segmentation performance evaluation |
| url | https://www.tandfonline.com/doi/10.1080/21642583.2024.2437162 |
| work_keys_str_mv | AT ayanpaul advancedsegmentationmodelsforautomatedcapsicumpeduncledetectioninnighttimegreenhouseenvironments AT rajendramachavaram advancedsegmentationmodelsforautomatedcapsicumpeduncledetectioninnighttimegreenhouseenvironments |