通过虫体花粉识别构建植物‒传粉者网络: 人工模型与AI模型高度一致

doi:10.17520/biods.2024088

摘要/Abstract

摘要：

传粉是生态系统中一项关键服务, 准确识别和分析传粉者携带的花粉对于理解植物‒传粉者交互作用以及传粉服务至关重要。传统的花粉识别方法依赖于显微镜下的人工直接观察, 这种方法耗时且需要专业知识, 限制了其在大规模应用中的效率, 在评估传粉效率和稀有植物‒传粉者连接方面存在局限性。针对此问题, 我们使用公共平台训练了基于河南洛阳市天池山国家森林公园14种同时开花植物的花粉识别人工智能(AI)模型, 通过比较人工显微镜观察和AI模型识别142只传粉者身体携带的花粉构成, 首次探讨了两种方法构建的植物‒传粉者互作网络的结构差异。结果表明AI模型在构建时能够达到96%的整体准确率。人工识别与AI模型在识别的连接数量、花粉数量以及图片一致率方面存在差异。AI模型在识别连接和花粉数量上略高于人工方法, 并且在第三方的一致性检验中, 超过半数的情况倾向于AI模型的结果。尽管存在一些独有连接的差异, 人工识别与AI模型构建的定量网络在结构特征上展现出高度的相似性。本研究揭示了AI图像识别技术对提高花粉分析效率和准确性的作用, 以及应用于植物‒传粉者互作研究的潜力, 这将有助于传粉网络研究的大规模开展, 为传粉生态学研究提供新的工具和视角。

关键词: AI模型, 花粉鉴定, 图像识别, 植物-传粉者互作, 虫体携带花粉

Abstract

Aims: Pollination plays a crucial role within ecosystems. Accurate identification and analysis of the pollen loads carried by pollinators are essential for understanding the plant‒pollinator interactions and assessing pollination activity. Traditional pollen identification methods, which are often reliant on microscopic observation, are time-consuming and require specialized expertise, limiting their utility in large-scale applications and in the assessment of pollination efficiency and rare plant‒pollinator interactions. To address this issue, we developed an Artificial Intelligence (AI) pollen identification model using a public platform.
Method: The AI model was based on pollen from 14 co-flowering plant species from Tianchi Mountain National Forest Park in Luoyang, China. We identified the pollen composition of 142 pollinators using both the traditional microscopic observation method and the AI model to explore and compare for the first time the structural differences in the plant‒pollinator interaction networks constructed by the two identification methods.
Results: The results demonstrated that the AI model achieved an overall accuracy rate of 96%. While there were differences between human recognition and the AI model in the number of identified links, quantity of pollen, and consistency rate of photo identification, both methods showed a high degree of consistency. The AI model slightly outperformed human methods in link identification (6.5%) and pollen quantity (0.8%). Moreover, in third-party consistency checks, the majority of the cases favored the results from the AI model. Despite some differences regarding unique links, the quantitative networks constructed by human recognition and the AI model showed a high degree of structural similarity.
Conclusion: This study reveals the potential of AI image recognition technology to enhance the efficiency and accuracy of pollen analysis, and its relevance to plant‒pollinator interaction research. This advancement could facilitate a more efficient advancement of large-scale studies of pollination networks, providing new tools and perspectives for pollination ecology research.

Key words: AI model, pollen identification, image recognition, plant-pollinator interaction, pollinator pollen load

巴苏艳, 赵春艳, 刘媛, 方强 (2024) 通过虫体花粉识别构建植物‒传粉者网络: 人工模型与AI模型高度一致. 生物多样性, 32, 24088. DOI: 10.17520/biods.2024088.

Suyan Ba, Chunyan Zhao, Yuan Liu, Qiang Fang (2024) Constructing a pollination network by identifying pollen on insect bodies: Consistency between human recognition and an AI model. Biodiversity Science, 32, 24088. DOI: 10.17520/biods.2024088.

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链接本文: https://www.biodiversity-science.net/CN/10.17520/biods.2024088

https://www.biodiversity-science.net/CN/Y2024/V32/I6/24088

图/表 7

表1 基于天池山花粉识别库进行的图像分割AI模型和物体检测AI模型训练结果比较。平均精度均值(mean Average Precision, mAP)为对所有类别的精确度的平均值, 范围0‒1, 越接近1说明模型效果越好。精确率为正确预测的物体数与预测物体总数之比)。召回率为正确预测的物体数与真实物体数之比。

Table 1 Comparison of the training results of AI models for image segmentation and object detection based on the pollen identification bank of Tianchi Mountain. mAP (mean Average Precision) measures the average precision across all categories, with a range from 0 to 1, higher values indicate better model performance. Precision is the ratio of correctly predicted objects to the total number of predicted objects. Recall is the ratio of correctly predicted objects to the total number of actual objects.

模型选择 Model selection	平均精度均值 mAP	精确率 Precision	召回率 Recall
图像分割 Image segmentation	95.52%	91.52%	91.45%
物体检测 Object detection	99.25%	97.20%	93.92%

图1 AI模型的训练效果。(a)不同阈值下的模型准确率, 阈值为0.8时模型达到最高准确率96%。对某类别而言F1-score是指精确率和召回率的调和平均数, 此处为各类别F1-score的平均数。(b)‒(e) AI模型对不同物种花粉图像的识别准确率: (b)瓜木98.2%; (c)一年蓬87.5%; (d)繁缕96.4%; (e)花旗杆91.4%。

Fig. 1 AI model training effect. (a) Model accuracy under different thresholds, the highest accuracy of the model is 96% when the threshold is 0.8. F1-score refers to the harmonic average of accuracy and recall for a category, where the average of F1-score for each category is shown. (b)‒(e) The recognition accuracy of the AI model for pollen images of different species: (b) Alangium platanifolium, 98.2%; (c) Erigeron annuus, 87.5%; (d) Stellaria media, 96.4%; (e) Dontostemon dentatus, 91.4%.

表2 基于AI模型与人工识别的植物‒传粉者互作定量网络的参数比较。网络的连接度、加权嵌套度、特化值、聚类系数、Shannon指数等网络参数均差异较小。H2°和wNODF与随机构建的零模型网络进行比较, 评估了参数的显著性。**P < 0.01。

Table 2 Comparison of the parameters of weighted plant- pollinator interaction networks quantified by AI models and human recognition. There were no significant differences in network metrics such as connectivity, nestedness, specialization level, clustering coefficient, and Shannon index. The significance of parameters was evaluated by comparing H2° and weighted NODF (wNODF) with randomly constructed null model networks. **P < 0.01.

网络指标 Network parameter	AI 模型 AI model	人工识别 Human recognition
连接度 Connectance	0.188	0.181
加权连接度 Weighted connectance	0.045	0.045
物种连接量 Links per species	2.100	2.014
特化值H₂° Specialization value H₂°	0.643^**	0.641^**
加权嵌套度 wNODF	13.847^**	14.298^**
聚类系数 Cluster coefficient	0.143	0.143
连接密度 Linkage density	3.185	3.132
Shannon指数 Shannon diversity	3.350	3.298

表3 植物与传粉者在基于AI模型和人工识别花粉的定量植物‒传粉者互作网络中的位置相关性(Pearson correlation)。**P < 0.01。

Table 3 Pearson correlation of the positions of the plant and pollinator species in AI model and human recognition based on weighted pollination networks. **P < 0.01.

物种水平指数 Species-level index	Pearson相关性 Pearson correlation
传粉者 Pollinator
介数中心度 Betweenness centrality	0.881^**
接近中心度 Closeness centrality	0.878^**
特化值d° Specialization value d°	0.943^**
植物 Plant
介数中心度 Betweenness centrality	0.814^**
接近中心度 Closeness centrality	0.875^**
特化值d° Specialisztion value d°	0.980^**

图2 AI模型与人工识别的结果差异。(a)两种方法识别的相同与不同的花粉图像数量。识别不同的再经过第三方人工进行二次识别。(b)两种方法识别结果相同与不同的虫体携带花粉数量。识别不同的花粉, 再经过第三方人工进行二次识别。

Fig. 2 Differences between AI models and human recognition results. (a) The similarities and differences between AI models and human recognition of pollen images. The interaction difference between the AI model and the human recognition is then manually identified by a third party. (b) The similarities and differences in the number of pollen grains carried by insects as identified by AI model and human recognition. The pollen load difference between the AI model and the human identification is then manually identified by a third party.

图3 通过人工识别(a)和AI模型识别(b)构建的植物‒传粉者定性网络。图的上方表示传粉者种类(棕色: 鳞翅目; 红色: 膜翅目; 黄色: 鞘翅目; 紫色: 双翅目; 黑色: 其他); 下方表示植物物种, 中间连线代表植物‒传粉者存在互作关系: 灰色连线代表人工识别与AI模型识别连接相同, 红色代表该网络独有连接。物种缩写详见附录1。

Fig. 3 Qualitative plant‒pollinator network by human recognition (a) and by AI model (b). The top of the graph shows pollinator species (Brown: Lepidoptera; Red: Hymenoptera; Yellow: Coleoptera; Purple: Diptera; Black, Others). The bottom represents plant species and the middle line represents plant‒pollinator interaction. Grey lines represent interactions identified as the same by both human recognition and AI model, while red lines represent interactions unique to that network. For a detailed list of species abbreviations, see Appendix 1.

图4 通过人工识别(a)和AI模型识别(b)构建的植物‒传粉者定量网络。中间连线代表植物‒传粉者存在互作关系, 连线越宽,代表传粉者携带该植物花粉数量越多。图的上方表示传粉者种类(棕色: 鳞翅目; 红色: 膜翅目; 黄色: 鞘翅目; 紫色: 双翅目; 黑色: 其他), 下方表示植物物种, 物种缩写详见附录1。

Fig. 4 Quantitative plant‒pollinator network by human recognition (a) and by AI model (b). The middle lines represent the interaction between plants and pollinators, with wider lines indicating a greater quantity of pollen carried by the pollinator from that plant. The top of the graph shows pollinator species (Brown, Lepidoptera; Red, Hymenoptera; Yellow, Coleoptera; Purple, Diptera; Black, Others), the bottom represents plant species, and for a detailed list of species abbreviations, see Appendix 1.

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