An improved chilli pepper flower detection approach based on YOLOv8
Abstract Artificial pollination can considerably improve pollination success and boost chilli pepper fruit set and quality when grown in enclosed environments (e.g., greenhouses). Artificial pollination, on the other hand, raises production costs while also necessitating specific operating abilities...
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| Format: | Article |
| Language: | English |
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BMC
2025-05-01
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| Series: | Plant Methods |
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| Online Access: | https://doi.org/10.1186/s13007-025-01390-9 |
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| _version_ | 1850231299045851136 |
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| author | Zhi-Yong Wang Cui-Ping Zhang |
| author_facet | Zhi-Yong Wang Cui-Ping Zhang |
| author_sort | Zhi-Yong Wang |
| collection | DOAJ |
| description | Abstract Artificial pollination can considerably improve pollination success and boost chilli pepper fruit set and quality when grown in enclosed environments (e.g., greenhouses). Artificial pollination, on the other hand, raises production costs while also necessitating specific operating abilities. The precise and efficient identification of pepper blossoms is a critical step in the development of robotic pollinators or pollination drones. In this paper, we propose a pepper flower detection method based on YOLOv8 that incorporates multi-scale, attention, and conditional information. To begin, the CBAM structure that incorporates edge information is integrated into Backbone to expand the feature extraction receptive field and facilitate the learning of long-distance dependency. The BERT model is then used to encode conditional information, which is integrated into the backbone via the ELAN layer to assist the training and inference processes. Finally, an improved MPDIoU is applied to increase detection accuracy while increasing flexibility. The experimental results show that the modification enhances the network depth and reduces the number of parameters from 4M to 2.85M, while improving the mean average accuracy (mAP) by 3.1% over the baseline approach. The study’s findings can help in crop object detection. The chilli pepper flower dataset: https://drive.google.com/file/d/1cKNie_iAzx-K4iPLQRVdyiOKV1d9zHrF/view?usp=drive_link The source code is available in https://drive.google.com/drive/folders/1ubNnKu7PWYAdUXvbs4Z2OBAVcSAQ3WLd?usp=drive_link . |
| format | Article |
| id | doaj-art-1b7342c1eb2841c29ac9cdc86b623f1b |
| institution | OA Journals |
| issn | 1746-4811 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj-art-1b7342c1eb2841c29ac9cdc86b623f1b2025-08-20T02:03:35ZengBMCPlant Methods1746-48112025-05-0121111510.1186/s13007-025-01390-9An improved chilli pepper flower detection approach based on YOLOv8Zhi-Yong Wang0Cui-Ping Zhang1Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and TechnologyShandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and TechnologyAbstract Artificial pollination can considerably improve pollination success and boost chilli pepper fruit set and quality when grown in enclosed environments (e.g., greenhouses). Artificial pollination, on the other hand, raises production costs while also necessitating specific operating abilities. The precise and efficient identification of pepper blossoms is a critical step in the development of robotic pollinators or pollination drones. In this paper, we propose a pepper flower detection method based on YOLOv8 that incorporates multi-scale, attention, and conditional information. To begin, the CBAM structure that incorporates edge information is integrated into Backbone to expand the feature extraction receptive field and facilitate the learning of long-distance dependency. The BERT model is then used to encode conditional information, which is integrated into the backbone via the ELAN layer to assist the training and inference processes. Finally, an improved MPDIoU is applied to increase detection accuracy while increasing flexibility. The experimental results show that the modification enhances the network depth and reduces the number of parameters from 4M to 2.85M, while improving the mean average accuracy (mAP) by 3.1% over the baseline approach. The study’s findings can help in crop object detection. The chilli pepper flower dataset: https://drive.google.com/file/d/1cKNie_iAzx-K4iPLQRVdyiOKV1d9zHrF/view?usp=drive_link The source code is available in https://drive.google.com/drive/folders/1ubNnKu7PWYAdUXvbs4Z2OBAVcSAQ3WLd?usp=drive_link .https://doi.org/10.1186/s13007-025-01390-9Deep learningAgriculture image analysisCrop detectionYOLO |
| spellingShingle | Zhi-Yong Wang Cui-Ping Zhang An improved chilli pepper flower detection approach based on YOLOv8 Plant Methods Deep learning Agriculture image analysis Crop detection YOLO |
| title | An improved chilli pepper flower detection approach based on YOLOv8 |
| title_full | An improved chilli pepper flower detection approach based on YOLOv8 |
| title_fullStr | An improved chilli pepper flower detection approach based on YOLOv8 |
| title_full_unstemmed | An improved chilli pepper flower detection approach based on YOLOv8 |
| title_short | An improved chilli pepper flower detection approach based on YOLOv8 |
| title_sort | improved chilli pepper flower detection approach based on yolov8 |
| topic | Deep learning Agriculture image analysis Crop detection YOLO |
| url | https://doi.org/10.1186/s13007-025-01390-9 |
| work_keys_str_mv | AT zhiyongwang animprovedchillipepperflowerdetectionapproachbasedonyolov8 AT cuipingzhang animprovedchillipepperflowerdetectionapproachbasedonyolov8 AT zhiyongwang improvedchillipepperflowerdetectionapproachbasedonyolov8 AT cuipingzhang improvedchillipepperflowerdetectionapproachbasedonyolov8 |