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  

Constructing a pollination network by identifying pollen on insect bodies:Consistency between human recognition and an AI model

作　　者：巴苏艳 赵春艳 刘媛 方强[1] Suyan Ba;Chunyan Zhao;Yuan Liu;Qiang Fang(College of Agriculture,Henan University of Science and Technology,Luoyang,Henan 471000)

机构地区：[1]河南科技大学农学院,河南洛阳471000

出　　处：《生物多样性》2024年第6期1-12,共12页Biodiversity Science

基　　金：河南省高等学校重点科研项目计划(24ZX001);国家自然科学基金(32371602)。

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。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

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 

分 类 号：Q948[生物学—植物学]