首个可可花访客图像数据集，助力智能识别授粉昆虫。
Identifying Cocoa Pollinators: A Deep Learning Dataset
- 从2300万张实地图像中构建5792张访客图像数据集
- 中等YOLOv8模型在8%背景图时达F1 0.71、mAP50 0.70
- 适合研究低对比度下昆虫检测的模型与可持续农业应用
可可产业年产值超数十亿美元，但通过授粉提升产量的研究仍有限。新型嵌入式硬件与基于AI的数据分析正推动对可可花访客种类及其对产量影响的认知。本文提出首个可可花访客数据集，包含5,792张蠓科、蚁科、蚜科、蜘蛛科和姬蜂科昆虫图像，以及1,082张背景可可花图像。数据源自中国海南产区两年间通过嵌入式摄像头采集的2300万张图像。我们以不同规模的YOLOv8模型进行实验，并逐步增加训练集中背景图像比例，以确定最优模型表现。中等尺寸的YOLOv8模型在仅8%背景图像时达到最佳性能（F1 Score 0.71，mAP50 0.70）。该数据集有助于评估深度学习模型在低对比度、难检测目标下的表现，可为未来基于授粉监测的可持续可可生产提供支持。
原文摘要 · Abstract (English)
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.
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