Identifying Cocoa Flower Visitors: A Deep Learning Dataset
Abstract Cocoa is a multi-billion-dollar industry but research on improving yields through pollination remains limited. New embedded hardware and AI-based data analysis is advancing information on cocoa flower visitors, their identity and implications for yields. We present the first cocoa flower vi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05631-3 |
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| author | Wenxiu Xu Saba Ghorbani Barzegar Dong Sheng Manuel Toledo-Hernández ZhenZhong Lan Thomas Cherico Wanger |
| author_facet | Wenxiu Xu Saba Ghorbani Barzegar Dong Sheng Manuel Toledo-Hernández ZhenZhong Lan Thomas Cherico Wanger |
| author_sort | Wenxiu Xu |
| collection | DOAJ |
| description | Abstract Cocoa is a multi-billion-dollar industry but research on improving yields through pollination remains limited. New embedded hardware and AI-based data analysis is advancing information on cocoa flower visitors, their identity and implications for yields. We present the first cocoa flower visitor dataset containing 5,792 images of Ceratopogonidae, Formicidae, Aphididae, Araneae, and Encyrtidae, and 1,082 background cocoa flower images. This dataset was curated from 23 million images collected over two years by embedded cameras in cocoa plantations in Hainan province, China. We exemplify the use of the dataset with different sizes of YOLOv8 models and by progressively increasing the background image ratio in the training set to identify the best-performing model. The medium-sized YOLOv8 model achieved the best results with 8% background images (F1 Score of 0.71, mAP50 of 0.70). Overall, this dataset is useful to compare the performance of deep learning model architectures on images with low contrast images and difficult detection targets. The data can support future efforts to advance sustainable cocoa production through pollination monitoring projects. |
| format | Article |
| id | doaj-art-e6b2d218429540a6afe056ba843ff7ee |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-e6b2d218429540a6afe056ba843ff7ee2025-08-20T03:04:22ZengNature PortfolioScientific Data2052-44632025-07-0112111010.1038/s41597-025-05631-3Identifying Cocoa Flower Visitors: A Deep Learning DatasetWenxiu Xu0Saba Ghorbani Barzegar1Dong Sheng2Manuel Toledo-Hernández3ZhenZhong Lan4Thomas Cherico Wanger5College of Environmental and Resource Sciences, Zhejiang UniversitySustainable Agricultural Systems & Engineering Laboratory, School of Engineering, Westlake UniversityCollege of Environmental and Resource Sciences, Zhejiang UniversitySustainable Agricultural Systems & Engineering Laboratory, School of Engineering, Westlake UniversitySchool of Engineering, Westlake UniversitySustainable Agricultural Systems & Engineering Laboratory, School of Engineering, Westlake UniversityAbstract Cocoa is a multi-billion-dollar industry but research on improving yields through pollination remains limited. New embedded hardware and AI-based data analysis is advancing information on cocoa flower visitors, their identity and implications for yields. We present the first cocoa flower visitor dataset containing 5,792 images of Ceratopogonidae, Formicidae, Aphididae, Araneae, and Encyrtidae, and 1,082 background cocoa flower images. This dataset was curated from 23 million images collected over two years by embedded cameras in cocoa plantations in Hainan province, China. We exemplify the use of the dataset with different sizes of YOLOv8 models and by progressively increasing the background image ratio in the training set to identify the best-performing model. The medium-sized YOLOv8 model achieved the best results with 8% background images (F1 Score of 0.71, mAP50 of 0.70). Overall, this dataset is useful to compare the performance of deep learning model architectures on images with low contrast images and difficult detection targets. The data can support future efforts to advance sustainable cocoa production through pollination monitoring projects.https://doi.org/10.1038/s41597-025-05631-3 |
| spellingShingle | Wenxiu Xu Saba Ghorbani Barzegar Dong Sheng Manuel Toledo-Hernández ZhenZhong Lan Thomas Cherico Wanger Identifying Cocoa Flower Visitors: A Deep Learning Dataset Scientific Data |
| title | Identifying Cocoa Flower Visitors: A Deep Learning Dataset |
| title_full | Identifying Cocoa Flower Visitors: A Deep Learning Dataset |
| title_fullStr | Identifying Cocoa Flower Visitors: A Deep Learning Dataset |
| title_full_unstemmed | Identifying Cocoa Flower Visitors: A Deep Learning Dataset |
| title_short | Identifying Cocoa Flower Visitors: A Deep Learning Dataset |
| title_sort | identifying cocoa flower visitors a deep learning dataset |
| url | https://doi.org/10.1038/s41597-025-05631-3 |
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