2026 한국식물분류학회

특별 심포지엄:통합 분류학적 AI를 향해서  Toward Integrative Taxonomic AI      

워크숍: AI 기반 식물표본 이미지 동정    AI-based Identification of Herbarium Images

          최근 식물분류학과 생물다양성 연구에서는 디지털 식물표본 이미지와 인공지능 기술의 활용이 빠르게 확대되고 있습니다. 고해상도 표본 이미지는 종 동정, 형태 분석, 표본관 자료 재검토, 기후변화 연구 등에 활용될 수 있는 중요한 연구 자원입니다. 이에 2026년 여름, 한국연구재단 한일공동연구 프로젝트와 한국식물분류학회의 후원으로 분류학에서 AI의 이용에 대한 특별 심포지엄과 워크숍을 개최합니다. 참가자들은 AI 활용 가능성을 살펴보고, 이미지 전처리, 모델 학습 및 추론, 결과 해석, 웹사이트 구축 과정을 실습하게 됩니다. 국제 심포지엄이기는 하지만, 대한민국 연사들은 한국어로, 일본 연사님들은 영어로 발표가 진행됩니다.

• 일시: 2026년 8월 18일 9:00~16:30

• 장소: 성신여자대학교 운정그린캠퍼스 C207, B-109 PC실

<Workshop: AI-based Identification of Herbarium Images> B-109 PC room

진행: Sangtae Kim / 김상태, Hyeonji Moon / 문현지, Se-Eun Jung / 정세은 (Sungshin Women’s University / 성신여자대학교)

         Sujeong Han / 한수정 (Chung-Ang University / 중앙대학교)

    워크숍 자료 PPT 다운로드    워크숍자료 PDF (바로보기)

    사용할 script 파일들 다운로드

    분류군을 지정하지 않은 분들을 위한  Viola 학습 data 다운로드      Viola 추론 data 다운로드  

    14:00–14:30 Introduction to the Overall Workshop Process

                      워크숍 전체 과정 소개

    14:30–15:00 Checking Computer Settings and Personal Preparation

                      개인 컴퓨터 환경설정 점검 및 실습 준비

    15:00–15:30 Preprocessing (HerbPAI) and AI Model Training

                      전처리(HerbPAI) and AI 모델학습

    15:30–16:00 Interpretation of Results, Model Inference, and Website Construction

                      결과 해석, 모델 추론, 및 웹사이트 구축

    16:00–16:30 Q & A

                      질의 응답

식물표본 AI 분석 관련 참고문헌

Al-Shamayleh AS, Ibrahim RW (2025) Grapevine disease detection using (q,τ)-nabla calculus quantum deformation with deep learning features. MethodsX 15:103619. doi: 10.1016/j.mex.2025.103619

Ariouat H, Pimpare EP, Chenin E et al (2025a)  Interpretable deep learning for botanical traits: a comparative study on the role of segmentation in herbarium image analysis. Procedia Comput Sci 270:3738–3747

Ariouat H, Sklab Y, Prifti E et al (2025b) Enhancing plant morphological trait identification in herbarium collections through deep learning–based segmentation. Appl Plant Sci 13:e70000. doi: 10.1002/aps3.70000

Arno J, Morel J, Rasaminirina F et al (2025) A pipeline to compile expert‐verified datasets of digitised herbarium specimens for automated plant identification to accelerate taxonomy. Plants People Planet. doi: 10.1002/ppp3.70149

Baek Y, Lee B, Han D et al (2019) Character region awareness for text detection. In: Proc IEEE/CVF Conf Comput Vis Pattern Recognit, 9365–9374

Bateux Q, Koss J, Sweeney PW et al (2025) Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process. PLoS Comput Biol 21:e1013650

Bebber DP, Carine MA, Wood JPI et al (2010) Herbaria are a major frontier for species discovery. Proc Natl Acad Sci USA 107:22169–22171. doi: 10.1073/pnas.1011841108

Belhumeur PN, Chen D, Feiner S et al (2008) Searching the world's herbaria: a system for visual identification of plant species. In: Forsyth D, Torr P, Zisserman A (Eds.): ECCV 2008, part IV, LNCS 5305, 116–129

Bissacco A, Cummins M, Netzer Y, Neven H (2013) Photoocr: reading text in uncontrolled conditions. In: Proc IEEE Int Conf Comput Vis, 785–792

Blanco M, Ruiz-Santaquiteria J, Cristóbal G et al (2025) Multimodal deep learning for cyanobacteria classification: a fusion of CNN and transformer architectures. Aquat Ecol 59:1319–1339

Budd GE (2021) Morphospace. Curr Biol 31:R1141–R1185 

Carranza-Rojas J, Goëau H, Bonnet P et al (2017) Going deeper in the automated identification of herbarium specimens. BMC Evol Biol 17:181

Carranza-Rojas J, Joly A, Goëau H et al (2018) Automated identification of herbarium specimens at different taxonomic levels. In: Multimedia Tools and Applications for Environmental & Biodiversity Informatics, Springer, 151–167

Cavanagh H, Mosbach A, Scalliet G et al (2021) Physics-informed deep learning characterizes morphodynamics of Asian soybean rust disease. Nat Commun 12:6424

Chagi K, Kawai H (2025) Deciphering handwritten herbarium specimen labels using vision-language models. Poster session abstract (P2-184), Ecol Soc Japan 72nd annual meeting. https://esj.ne.jp/meeting/abst/72/P2-184.html (in Japanese)

Chen X, Wang X, Changpinyo S et al (2021) How to train your ViT? Data, augmentation, and regularization in vision transformers. arXiv:2106.10270 doi: 10.48550/arXiv.2106.10270

Christiansen P, Nielsen LN, Steen KA et al (2016) DeepAnomaly: combining background subtraction and deep learning for detecting obstacles and anomalies in an agricultural field. Sensors 16:1904. doi: 10.3390/s16111904

Chulif S, Lee SH, Chang YL, Chai KC (2023) A machine learning approach for cross-domain plant identification using herbarium specimens. Neural Comput Appl 35:5963–5985

Colutto S, Kahle P, Guenter H, Muehlberger G (2019) Transkribus. A platform for automated text recognition and searching of historical documents. In: Proc 15th Int Conf eScience, 463–466

d'Ascoli S, Touvron H, Leavitt M et al (2021) ConViT: Improving vision transformers with soft convolutional inductive biases. arXiv:2103.10697 doi: 10.48550/arXiv.2103.10697

Davis CC, Champ J, Park DS et al (2020) A new method for counting reproductive structures in digitized herbarium specimens using Mask R-CNN. Front Plant Sci 11:1129

de Lutio R, Park JY, Watson KA et al (2022) The Herbarium 2021 Half-Earth Challenge dataset and machine learning competition. Front Plant Sci 12:787127

Deng J, Guo J, Liu T et al (2020) Sub-center arcface: Boosting face recognition by large-scale noisy web faces. In: Vedaldi A, Bischof H, Brox T, Frahm JM (eds) European Conference on Computer Vision. Springer, Cham, pp 741–757

Dosovitskiy A, Lucas B, Kolesnikov A et al (2021) An image is worth 16x16 words: transformers for image recognition at scale. arXiv:2010.11929 doi: 10.48550/arXiv.2010.11929

Drinkwater R, Cubey RWN, Haston E (2014) The use of Optical Character Recognition (OCR) in the digitisation of herbarium specimen labels. PhytoKeys 38:15–30. doi: 10.3897/phytokeys.38.7168

Edwards M, Morse DR (1995) The potential for computer-aided identification in biodiversity research. Trends Ecol Evol 10:153–158

Fadilah N, Mohamad-Saleh J, Abdul Halim Z et al (2012) Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch. Sensors 12:14179–14195. doi: 10.3390/s121014179

Geronimo JO, Arguelles EDLR, Abriol-Santos KJM (2023) Automated Classification and Identification System for Freshwater Algae Using Convolutional Neural Networks. Philipp J Sci 152:325–335. doi: 10.56899/152.01.25

Goëau H, Mora‐Fallas A, Champ J et al (2020) A new fine‐grained method for automated visual analysis of herbarium specimens: a case study for phenological data extraction. Appl Plant Sci 8:e11368. doi: 10.1002/aps3.11368

Goyal SR, Kulkarni VS, Choudhary R, Jain R (2025) A comparative analysis of efficacy of machine learning techniques for disease detection in some economically important crops. Crop Prot 190:107093. doi: 10.1016/j.cropro.2024.107093

Guo YY, Cai H, Bramley GL et al (2025) A review of artificial intelligence in herbarium specimen image analysis. Art Intell Rev 58:402

Guralnick RP, Lafrance R, Allen JM, Denslow MW (2025) Ensemble automated approaches for producing high-quality herbarium digital records. Appl Plant Sci 13:e11623

Hackel J, Imhof S, Zizka A (2026) Teaching plant identification at a university in the age of artificial intelligence. Plants People Planet 8:20–25

Han S, Moon H, Lee S et al (2026) A survey of neural network segmentation and validation on plant specimen images of Korean Violaceae. Expert Syst Appl 296:128909

Hart R, Salick J, Ranjitkar S, Xu J (2014) Herbarium specimens show contrasting phenological responses to Himalayan climate. Proc Natl Acad Sci USA 111:10615–10619. doi: 10.1073/pnas.1403376111

He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit (CVPR 2016), 770–778.

Heberling JM, Prather LA, Tonsor SJ (2019) The changing uses of herbarium data in an era of global change: an overview using automated content analysis. Bioscience 69:812–822. doi: 10.1093/biosci/biz094

Hollister JD, Martin G, Cai X et al (2025) A computer vision method for finding mislabelled specimens within natural history collections. Ecol Evol 15:e71648.

Hu Y, Hongchen Z, Changming L et al (2025) Classification of maize seed hyperspectral images based on variable-depth convolutional kernels. Front Plant Sci 16:1599231

Hussein BR, Malik OA, Ong W-H, Slik JWF (2022) Application of computer vision and machine learning for digitized herbarium specimens: a systematic literature review. Ecol Inform 69:101641. doi: 10.1016/j.ecoinf.2022.101641

Hussein BR, Malik OA, Ong WH, Slik JWF (2021) Automated extraction of phenotypic leaf traits of individual intact herbarium leaves from herbarium specimen images using deep learning based semantic segmentation. Sensors 21:4549. doi: 10.3390/s21134549

Javed H, Fahad LG, Tahir SF et al (2025) Enhanced Plant Species Identification through Metadata Fusion and Vision Transformer Integration. Comput Mater Contin 85:3981–3996. doi: 10.32604/cmc.2025.064359

Joly A, Pierre B, Goëau H et al (2016) A look inside the Pl@ntNet experience. Multimed Syst 22. doi: 10.1007/s00530-015-0462-9

Kajihara AY, Queiroz GAD, Caxambú MG et al (2025) A database for automatic identification of herbarium specimens in Piperaceae family. Multimed Tools Appl 1–29

Karbstein K, Kösters L, Hodač L et al (2024) Species delimitation 4.0: integrative taxonomy meets artificial intelligence. Trends Ecol Evol 39:771–784

Katal N, Rzanny M, Mäder P, Wäldchen J (2022) Deep learning in plant phenological research: a systematic literature review. Front Plant Sci 13:805738

Kösters LM, Karbstein K, Hofmann M et al (2025) Data fusion for integrative species identification using deep learning. Syst Biol syaf026. doi: 10.1093/sysbio/syaf026

Kumar N, Belhumeur PN, Biswas A et al (2012) Leafsnap: a computer vision system for automatic plant species identification. In: Fitzgibbon A, Lazbnik S, Perona P et al (eds) Computer Vision – ECCV 2012. LNCS 7573. Springer, Berlin, Heidelberg. doi: 10.1007/978-3-642-33709-3_36

Lee S, Moon H, Kim S, Lee J (2025) Multiple kernel-enhanced encoder for effective herbarium image segmentation. Electron Lett 61:e70155. doi: 10.1049/ell2.70155

Lesperance N, Ratnasingham S, Taylor G (2025) Taxonomic Reasoning for Rare Arthropods: Combining Dense Image Captioning and RAG for Interpretable Classification. arXiv:2503.10886 doi: 10.48550/arXiv.2503.10886

Lhayani K, Rabeh K, Medraoui L (2025) Deep learning for Fabaceae identification by integrating molecular and morphological data and a solution for barcode selection. J Syst Evol. doi: 10.1111/jse.70021

Li B, Xia R, Li J et al (2025) Multimodal deep learning with hyperspectral imaging for accurate origin classification of wolfberries. Food Chem X 31:103166

Li M, Lv T, Chen J et al (2023) Trocr: transformer-based optical character recognition with pre-trained models. In: Proc AAAI Conf Artif Intell, 37(11): 13094–13102. doi: 10.1609/aaai.v37i11.26538

Li W, Tojibaev KS, Hisoriev H et al (2020) Mapping Asia Plants: Current status of floristic information for Central Asian flora. Glob Ecol Conserv 24:e01220

Li Y, Zhao D, Qiao T et al (2025) MetaWild: A Multimodal Dataset for Animal Re-Identification with Environmental Metadata. In: Proc ACM Int Conf Multimed, 13009–13015. doi: 10.1145/3746027.3758249

Li Y, Zou D, Shrestha N et al (2020) Spatiotemporal variation in leaf size and shape in response to climate. J Plant Ecol 13:87–96

Little DP, Aguero B, Shaw AJ, Tessler M (2025) AI for difficult herbarium specimens: identification of peat mosses (subgenus Sphagnum) without dissection. New Phytol. doi  10.1111/nph.70461

Little DP, Tulig M, Tan KC et al (2020) An algorithm competition for automatic species identification from herbarium specimens. Appl Plant Sci 8:e11365

Liu C, Liu W, Lu X et al (2014) Application of multispectral imaging to determine quality attributes and ripeness stage in strawberry fruit. PLoS ONE 9:e87818. doi: 10.1371/journal.pone.0087818

Lorieul T, Pearson KD, Ellwood ER et al (2019) Toward a large‐scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras. Appl Plant Sci 7:e01233. doi: 10.1002/aps3.1233

Luo H, Zhang X, Yuan F et al (2025) MOSSNet: a lightweight dual-branch multi-scale attention neural network for bryophyte identification. Symmetry 17:347. doi: 10.3390/sym17030347

Mäder P, Boho D, Rzanny M et al (2021) The flora incognita app–interactive plant species identification. Methods Ecol Evol 12:1335–1342

Manolache G, Schouten G, Vanschoren J (2025) CrypticBio: A Large Multimodal Dataset for Visually Confusing Species. arXiv:2505.14707 doi: 10.48550/arXiv.2505.14707

Marcos D, van de Vlasakker R, Athanasiadis IN et al (2025) Fully automatic extraction of morphological traits from the web: Utopia or reality?. Appl Plant Sci 13:e70005. doi: 10.1002/aps3.70005

Masuda H, Jukei T, Hasegawa T (2020) Fish species identification using a CNN-based multimodal learning method. In: Proc International Conference on Image, Video and Signal Processing (IVSP), 15–19. doi: 10.1145/3388818.3389164

Meineke EK, Classen AT, Sanders NJ, Davies TJ (2019) Herbarium specimens reveal increasing herbivory over the past century. J Ecol 107:105–117. doi: 10.1111/1365-2745.13057

Meineke EK, Tomasi C, Yuan S, Pryer KM (2020) Applying machine learning to investigate long-term insect–plant interactions preserved on digitized herbarium specimens. Appl Plant Sci 8:e11369

Mir TA, Bharany S, Gupta R et al (2025) Hybrid Deep Learning Model for Date Palm Disease Classification: A Fusion of HybridConv Mixer and Vision Transformer. Food Sci Nutr 13:e71086. doi: 10.1002/fsn3.71086

Morse LE (1971) Specimen identification and key construction with time-sharing computers. Taxon 20:269–282

Myers N, Mittermeier RA, Mittermeier CG et al (2000) Biodiversity hotspots for conservation priorities. Nature 403:853–858

Nainia A, Vignes-Lebbe R, Chenin E et al (2024) FloraNER: A new dataset for species and morphological terms named entity recognition in French botanical text. Data Brief 56:110824. doi: 10.1016/j.dib.2024.110824

Nelson G, Ellis S (2019) The history and impact of digitization and digital data mobilization on biodiversity research. Phil Trans R Soc B 374:2017391. doi: 10.1098/rstb.2017.0391

Ong CA, Tay FS, Then YL, McCarthy C (2025) An evaluation of a pre-trained transformer-based self-distillation model (DINOv2) for cross-domain plant species identification. Neural Comput Appl 37:21969–21995

Ott T, Palm C, Vogt R, Oberprieler C (2020) GinJinn: an object-detection pipeline for automated feature extraction from herbarium specimens. Appl Plant Sci 8:e11351

Pankhurst RJ (1991) Practical taxonomic computing. Cambridge University Press, Cambridge

Park J, de Lutio R, Rappazzo B et al (2024) NAFlora-1M: continental-scale high-resolution fine-grained plant classification dataset. J Data-Centric Mach Learn Res 1:1–21

Radford A, Kim J, Hallacy C (2021) Learning transferable visual models from natural language supervision. In: Proceedings of the 38th International Conference on Machine Learning (ICML 2021), PMLR 139: 8748–8763

Raman R, Jayaraman S (2025) Artificial intelligence for sustainable farming with dual branch convolutional graph attention networks in rice leaf disease detection. Sci Rep 15:10595. doi: 10.1038/s41598-025-94891-5

Raup DM (1966) Geometric analysis of shell coiling: general problems. J Paleontol 40:1178–1190

Renner S (2025) Linking to images and AI-based identification tools- The only way for flora projects to survive. Plants People Planet. doi: 10.1002/ppp3.70131

Pearson KD, Nelson G, Aronson MF et al (2020) Machine learning using digitized herbarium specimens to advance phenological research. BioScience 70:610-620

Russakovsky O, Deng J, Su H et al (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115:211–252. doi: 10.1007/s11263-015-0816-y

Sadek J, Vlachidis A, Pickering V et al (2024) Leveraging OCR and HTR cloud services towards data mobilisation of historical plant names. Int J Digit Humanit 6:237–261. doi: 10.1007/s42803-024-00091-4

Sahraoui M, Sklab Y, Pignal M et al (2023) Leveraging multimodality for biodiversity data: exploring joint representations of species descriptions and specimen images using CLIP. Biodiver Inf Sci Stand 7:e112666

Sastry S, Khanal S, Dhakal A et al (2025) TaxaBind: A unified embedding space for ecological applications. In: IEEE Winter Conf Appl Comput Vis, 1765–1774. doi: 10.1109/WACV61041.2025.00179

Schmidt-Lebuhn AN, Knerr N (2025) Large Language Models can extract morphological data from taxonomic descriptions, but their stochastic nature makes automation challenging: a test on Australian Asteraceae. PhytoKeys 261:189–210

Selvaraju RR, Cogswell M, Das A et al (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proc IEEE Int Conf Comput Vis, 618–626

Shirai M, Takano A, Kurosawa T et al (2022) Development of a system for the automated identification of herbarium specimens with high accuracy. Sci Rep 12:8066

Sklab Y, Castanet F, Ariouat H et al (2025) PlantSAM: an object detection‐driven segmentation pipeline for herbarium specimens. Appl Plant Sci e70034. doi: 10.1002/aps3.70034

Sladojevic S, Arsenovic M, Anderla A et al (2016) Deep neural networks based recognition of plant diseases by leaf image classification. Comp Intell Neurosci 2016:3289801

Soltis PS, Nelson G, Zare A et al (2020) Plants meet machnes: prospects in machine learning for plant biology. Appl Plant Sci 8:e11371. doi:10.1002/aps3.11371

Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45: 427–437. doi: 10.1016/j.ipm.2009.03.002

Stenhouse A, Thrall PH (2025) SpeciMate: improving metadata extraction from digitised biological specimens. Biodiv Data J 13:e160553

Sun J, Liu B, Rustiami H et al (2024) Mapping Asia plants: Plant diversity and a checklist of vascular plants in Indonesia. Plants 13:2281

Szegedy C, Liu W, Jia Y et al (2015) Going deeper with convolutions. In: Proc IEEE Conf Comput Vis Pattern Recognit, 1–9

Takano A, Cole TCH, Konagai H (2024) A novel automated label data extraction and database generation system from herbarium specimen images using OCR and NER. Sci Rep 14:112

Takano A, Horiuchi Y, Fujimoto Y et al (2019). Simple but long-lasting: A specimen imaging method applicable for small-and medium-sized herbaria. PhytoKeys 118:1–14

Takano et al. (2026) Herbarium specimens in the age of artificial intelligence: from herbarium image identification to integrative taxonomic AI. Journal of Plant Research  In Press.

Tan KC, Liu Y, Ambroose B et al (2019) The Herbarium Challenge 2019 dataset. arXiv:1906.05372 doi: 10.48550/arXiv.1906.05372

Tan M, Le QV (2019) EfficientNet: Rethinking model scaling for convolutional neural networks. In: Proc Mach Learn Res, 97:6105–6114

Tessler M, Little DP (2025) On herbarium specimen images and artificial intelligence. New Phytol. doi: 10.1111/nph.70312

Thiers BM (2024) Strengthening partnerships to safeguard the future of herbaria. Diversity 16:36

Thiers BM (2025) The World's herbaria 2024: a summary report based on data from Index Herbariorum. Technical Report, William and Lynda Steere Herbarium, New York Botanical Garden. https://sweetgum.nybg.org/science/wp-content/uploads/2025/01/TheWorldHerbaria2024-.pdf (Accessed November 21 2025)

Tietje M, Antonelli A, Forest F et al (2023) Global hotspots of plant phylogenetic diversity. New Phytol 240:1636–1646

Topaloglu F, Kiziloluk S, Sert E, Yildirim M (2025) Automatic detection of harmful cyanobacterial genera using deep CNN models and artemisinin optimization. Sci Rep 15:33824

Triki A, Bouaziz B, Mahdi W (2022a) A deep learning-based approach for detecting plant organs from digitized herbarium specimen images. Ecol Inform 69:101590

Triki A, Bouaziz B, Mahdi W et al (2022b) Deep learning based approach for digitized herbarium specimen segmentation. Multimed Tools Appl 81:28689–28707. doi: 10.1007/s11042-022-12711-6

Turnbull R, Fitzgerald E, Thompson KM, Birch JL (2025) Hespi: A pipeline for automatically detecting information from herbarium specimen sheets. BioScience 75:637–648

Vaishnav M, Fel T, Rodríguez IF, Serre T (2022) Conviformers: convolutionally guided vision transformer. arXiv:2208.08900 doi: 10.48550/arXiv.2208.08900

Wäldchen J, Rzanny M, Seeland M, Mäder P (2018) Automated plant species identification—Trends and future directions. PLoS Comput Biol 14:e1005993

Wang H, Soejima A, Chang Ks, Ma K (2020) Mapping Asia plants: Current status of floristic information for Northeast Asia. Glob Ecol Conserv 24:e01321

Wang SQ, Dong XY, Ye L et al (2023) Flora of Northeast Asia. Plants 12:2240

Weaver WN, Ng J, Laport RG (2020) LeafMachine: using machine learning to automate leaf trait extraction from digitized herbarium specimens. Appl Plant Sci 8:e11367

Weaver WN, Smith SA (2023) From leaves to labels: building modular machine learning networks for rapid herbarium specimen analysis with LeafMachine2. Appl Plant Sci 11:e11548

Wen X, Liu Q, Xuanyuan T et al (2025) A lightweight convolutional neural network for tea leaf disease and pest recognition. Plant Methods 21. doi: 10.1186/s13007-025-01452-y

White AE, Dikow RB, Baugh M et al (2020) Generating segmentation masks of herbarium specimens and a data set for training segmentation models using deep learning. Appl Plant Sci 8:e11352. doi: 10.1002/aps3.11352

White S, Feiner S, Kopylec J (2006) Virtual vouchers: Prototyping a mobile augmented reality user interface for botanical species identification. In: Proc IEEE Symp on 3DUI, 119–126

Wieczorek J, Bloom D, Guralnick R et al (2012) Darwin core: an evolving community-developed biodiversity data standard. PLoS ONE 7: e29715. doi: 10.1371/journal.pone.0029715

Wilde BC, Bragg JG, Cornwell W (2023)  Analyzing trait–climate relationships within and among taxa using machine learning and herbarium specimens. Am J Bot 110:e16167

Williamson DR, Prestø T, Westergaard KB et al (2025) Long-term trends in global flowering phenology. New Phytol. doi: 10.1111/nph.70139

Wright IJ, Dong N, Maire V et al (2017) Global climatic drivers of leaf size. Science 357:917–921

Wu H, Xiao B, Codella N.  et al (2021) CvT: Introducing convolutions into vision transformers. arXiv:2103.15808 doi: 10.48550/arXiv.2103.15808

Xiao C, Zhang Z, Ma K, Lin Q (2023) Mapping Asia plants: Historical outline and review of sources on floristic diversity in South Asia. Plants 12:1617

Xie G, Xuan J, Luo HR et al (2025) AI-based identification of Chinese vascular plants from herbarium specimens: a tool for all herbaria with Chinese holdings. New Phytol. doi: 10.1111/nph.70443

Xu X, Naqinezhad A, Ghazanfar SA et al (2020) Mapping Asia plants: Current status on floristic information in Southwest Asia. Glob Ecol Conserv 24:e01257

Xue J, Chepinoga VV, Liu Y, Ma K (2020) Mapping Asia Plants: Historical outline and review of sources on floristic diversity in North Asia (Asian Russia). Glob Ecol Conserv 24:e01287

Yang B, Zhang Z, Yang CQ et al (2022) Identification of species by combining molecular and morphological data using convolutional neural networks. Syst Biol 71:690–705

Younis S, Schmidt M, Weiland C et al (2020) Detection and annotation of plant organs from digitised herbarium scans using deep learning. Biodivers Data J 8:e57090

Younis S, Weiland C, Hoehndorf R et al (2018) Taxon and trait recognition from digitized herbarium specimens using deep convolutional neural networks. Bot Lett 165:377–383

Zhang Y, Ruofei B, Mengxin G et al (2025) A lightweight deep convolutional neural network development for soybean leaf disease recognition. Front Plant Sci 16. doi: 10.3389/fpls.2025.1655564

Zhou X, Yao C, Wen H et al (2017) East: an efficient and accurate scene text detector. In: Proc IEEE Conf Comput Vis Pattern Recognit, 5551–5560