{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T10:46:09Z","timestamp":1775645169296,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T00:00:00Z","timestamp":1739232000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T00:00:00Z","timestamp":1739232000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62101268"],"award-info":[{"award-number":["62101268"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82204770"],"award-info":[{"award-number":["82204770"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"China Agriculture Research System of MOF and MARA","award":["CARS-21"],"award-info":[{"award-number":["CARS-21"]}]},{"name":"Jiangsu Province 333 High-level Talents Training Project"},{"name":"\"Qing Lan Project\" in Colleges and universities in Jiangsu"},{"name":"Youth Science Foundation of Jiangsu Province","award":["BK20210696"],"award-info":[{"award-number":["BK20210696"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s10044-025-01419-8","type":"journal-article","created":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T19:05:21Z","timestamp":1739300721000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Bidirectional feature fusion via cross-attention transformer for chrysanthemum classification"],"prefix":"10.1007","volume":"28","author":[{"given":"Yifan","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xichen","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongyuan","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianshu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,11]]},"reference":[{"issue":"8","key":"1419_CR1","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.3390\/antiox10081309","volume":"10","author":"J Sun","year":"2021","unstructured":"Sun J, Wang Z, Chen L, Sun G (2021) Hypolipidemic effects and preliminary mechanism of chrysanthemum flavonoids, its main components luteolin and luteoloside in hyperlipidemia rats. Antioxidants 10(8):1309","journal-title":"Antioxidants"},{"key":"1419_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jff.2021.104746","volume":"87","author":"J Zhan","year":"2021","unstructured":"Zhan J, He F, Cai H, Wu M, Xiao Y, Xiang F, Yang Y, Ye C, Wang S, Li S (2021) Composition and antifungal mechanism of essential oil from Chrysanthemum morifolium cv. fubaiju. J Funct Foods 87:104746","journal-title":"J Funct Foods"},{"issue":"10","key":"1419_CR3","doi-asserted-by":"publisher","first-page":"3038","DOI":"10.3390\/molecules26103038","volume":"26","author":"S Jiang","year":"2021","unstructured":"Jiang S, Wang M, Jiang Z, Zafar S, Xie Q, Yang Y, Liu Y, Yuan H, Jian Y, Wang W (2021) Chemistry and pharmacological activity of sesquiterpenoids from the chrysanthemum genus. Molecules 26(10):3038","journal-title":"Molecules"},{"issue":"10","key":"1419_CR4","doi-asserted-by":"publisher","first-page":"1460","DOI":"10.3390\/foods9101460","volume":"9","author":"FS Youssef","year":"2020","unstructured":"Youssef FS, Eid SY, Alshammari E, Ashour ML, Wink M, El-Readi MZ (2020) Chrysanthemum indicum and chrysanthemum morifolium: chemical composition of their essential oils and their potential use as natural preservatives with antimicrobial and antioxidant activities. Foods 9(10):1460","journal-title":"Foods"},{"issue":"9","key":"1419_CR5","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.3390\/app9091959","volume":"9","author":"J He","year":"2019","unstructured":"He J, Zhu S, Chu B, Bai X, Xiao Q, Zhang C, Gong J (2019) Nondestructive determination and visualization of quality attributes in fresh and dry chrysanthemum morifolium using near-infrared hyperspectral imaging. Appl Sci 9(9):1959","journal-title":"Appl Sci"},{"key":"1419_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.microc.2021.106464","volume":"168","author":"Y Chen","year":"2021","unstructured":"Chen Y, Zhen X-T, Yu Y-L, Shi M-Z, Cao J, Zheng H, Ye L-H (2021) Chemoinformatics based comprehensive two-dimensional liquid chromatography-quadrupole time-of-flight mass spectrometry approach to chemically distinguish chrysanthemum species. Microchem J 168:106464","journal-title":"Microchem J"},{"key":"1419_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.microc.2021.106464","volume":"168","author":"Y Chen","year":"2021","unstructured":"Chen Y, Zhen X-T, Yu Y-L, Shi M-Z, Cao J, Zheng H, Ye L-H (2021) Chemoinformatics based comprehensive two-dimensional liquid chromatography-quadrupole time-of-flight mass spectrometry approach to chemically distinguish chrysanthemum species. Microchem J 168:106464","journal-title":"Microchem J"},{"issue":"2","key":"1419_CR8","doi-asserted-by":"crossref","first-page":"105","DOI":"10.3901\/JME.2016.18.105","volume":"37","author":"G Zhai","year":"2016","unstructured":"Zhai G, Li Z, Lu W, Zhao Y, Wang C (2016) Study on varieties of identification ornamental chrysanthemum based on image processing. J Chin Agric Mech 37(2):105\u2013110","journal-title":"J Chin Agric Mech"},{"key":"1419_CR9","doi-asserted-by":"crossref","unstructured":"Long W, zhang Q, Wang S-R, Suo Y, Chen H, Bai X, Yang X, Zhou Y-P, Yang J, Fu H (2023) Fast and non-destructive discriminating the geographical origin of Hangbaiju by hyperspectral imaging combined with chemometrics. Spectrochim Acta Part A: Mol Biomol Spectrosc 284:121786","DOI":"10.1016\/j.saa.2022.121786"},{"key":"1419_CR10","unstructured":"Liu Z, Gao K, Tian y, Dai S, Song X (2017) Identification of Chrysanthemum cultivars based on unfolding image with lbp texture feature. Research progress of ornamental horticulture in China"},{"key":"1419_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106611","volume":"155","author":"R Hadipour-Rokni","year":"2023","unstructured":"Hadipour-Rokni R, Asli-Ardeh EA, Jahanbakhshi A, Sabzi S et al (2023) Intelligent detection of citrus fruit pests using machine vision system and convolutional neural network through transfer learning technique. Comput Biol Med 155:106611","journal-title":"Comput Biol Med"},{"key":"1419_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2022.101829","volume":"71","author":"M Momeny","year":"2022","unstructured":"Momeny M, Jahanbakhshi A, Neshat AA, Hadipour-Rokni R, Zhang Y-D, Ampatzidis Y (2022) Detection of citrus black spot disease and ripeness level in orange fruit using learning-to-augment incorporated deep networks. Eco Inform 71:101829","journal-title":"Eco Inform"},{"key":"1419_CR13","volume":"17","author":"R Azadnia","year":"2023","unstructured":"Azadnia R, Fouladi S, Jahanbakhshi A (2023) Intelligent detection and waste control of hawthorn fruit based on ripening level using machine vision system and deep learning techniques. Res Eng 17:100891","journal-title":"Res Eng"},{"key":"1419_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.foodcont.2022.109554","volume":"147","author":"M Momeny","year":"2023","unstructured":"Momeny M, Neshat AA, Jahanbakhshi A, Mahmoudi M, Ampatzidis Y, Radeva P (2023) Grading and fraud detection of saffron via learning-to-augment incorporated inception-v4 cnn. Food Control 147:109554","journal-title":"Food Control"},{"issue":"3","key":"1419_CR15","doi-asserted-by":"publisher","first-page":"507","DOI":"10.3390\/agriculture14030507","volume":"14","author":"Z Zhang","year":"2024","unstructured":"Zhang Z, Xiao J, Wang W, Zielinska M, Wang S, Liu Z, Zheng Z (2024) Automated grading of angelica sinensis using computer vision and machine learning techniques. Agriculture 14(3):507","journal-title":"Agriculture"},{"issue":"9","key":"1419_CR16","doi-asserted-by":"publisher","first-page":"1744","DOI":"10.3390\/agriculture13091744","volume":"13","author":"Z Zhang","year":"2023","unstructured":"Zhang Z, Xiao J, Wang S, Wu M, Wang W, Liu Z, Zheng Z (2023) Origin intelligent identification of angelica sinensis using machine vision and deep learning. Agriculture 13(9):1744","journal-title":"Agriculture"},{"issue":"8","key":"1419_CR17","doi-asserted-by":"publisher","first-page":"4499","DOI":"10.1109\/TNNLS.2021.3116209","volume":"34","author":"Z Xie","year":"2021","unstructured":"Xie Z, Zhang W, Sheng B, Li P, Chen CP (2021) Bagfn: broad attentive graph fusion network for high-order feature interactions. IEEE Trans Neural Netw Learn Syst 34(8):4499\u20134513","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1419_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-024-03570-5","author":"SG Ali","year":"2024","unstructured":"Ali SG, Wang X, Li P, Li H, Yang P, Jung Y, Qin J, Kim J, Sheng B (2024) Egdnet: an efficient glomerular detection network for multiple anomalous pathological feature in glomerulonephritis. Vis Comput. https:\/\/doi.org\/10.1007\/s00371-024-03570-5","journal-title":"Vis Comput"},{"key":"1419_CR19","first-page":"1","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inform Process Syst 25:1","journal-title":"Adv Neural Inform Process Syst"},{"key":"1419_CR20","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"1419_CR21","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1419_CR22","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1419_CR23","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"issue":"11","key":"1419_CR24","doi-asserted-by":"publisher","first-page":"9562","DOI":"10.1109\/TNNLS.2022.3158966","volume":"34","author":"J Xu","year":"2022","unstructured":"Xu J, Pan Y, Pan X, Hoi S, Yi Z, Xu Z (2022) Regnet: Self-regulated network for image classification. IEEE Trans Neural Netw Learn Syst 34(11):9562\u20139567","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1419_CR25","doi-asserted-by":"crossref","unstructured":"Wang C-Y, Liao H-YM, Wu Y-H, Chen P-Y, Hsieh J-W, Yeh I-H (2020) Cspnet: A new backbone that can enhance learning capability of cnn. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops, pp 390\u2013391","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"1419_CR26","unstructured":"Tan M (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946"},{"key":"1419_CR27","doi-asserted-by":"crossref","unstructured":"Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, Xie S (2023) Convnext v2: Co-designing and scaling convnets with masked autoencoders. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 16133\u201316142","DOI":"10.1109\/CVPR52729.2023.01548"},{"key":"1419_CR28","doi-asserted-by":"crossref","unstructured":"Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S (2022) A convnet for the 2020s. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11976\u201311986","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"1419_CR29","doi-asserted-by":"crossref","unstructured":"He K, Chen X, Xie S, Li Y, Doll\u00e1r P, Girshick R (2022) Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 16000\u201316009","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"1419_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106679","volume":"194","author":"P Yuan","year":"2022","unstructured":"Yuan P, Qian S, Zhai Z, Fern\u00e1nMart\u00ednez J, Xu H (2022) Study of chrysanthemum image phenotype on-line classification based on transfer learning and bilinear convolutional neural network. Comput Electron Agric 194:106679","journal-title":"Comput Electron Agric"},{"issue":"2","key":"1419_CR31","first-page":"258","volume":"55","author":"P Yuan","year":"2024","unstructured":"Yuan P, Ding Y, Xu H (2024) Fine-grained chrysanthemum phenotype recognition based on deep active learning and cbam. Trans Chin Soc Agric Mach 55(2):258\u2013267","journal-title":"Trans Chin Soc Agric Mach"},{"key":"1419_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13007-019-0532-7","volume":"15","author":"Z Liu","year":"2019","unstructured":"Liu Z, Wang J, Tian Y, Dai S (2019) Deep learning for image-based large-flowered chrysanthemum cultivar recognition. Plant Methods 15:1\u201311","journal-title":"Plant Methods"},{"key":"1419_CR33","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2022.806711","volume":"13","author":"J Wang","year":"2022","unstructured":"Wang J, Tian Y, Zhang R, Liu Z, Tian Y, Dai S (2022) Multi-information model for large-flowered chrysanthemum cultivar recognition and classification. Front Plant Sci 13:806711","journal-title":"Front Plant Sci"},{"key":"1419_CR34","doi-asserted-by":"crossref","unstructured":"Huang S, Liu G (2023) Research on fine-grained classification of chrysanthemum images based on multi-scale and multi-parallel convolutional neural network. In: International conference on cyber security, artificial intelligence, and digital economy (CSAIDE 2023), 12718, pp 430\u2013437. SPIE","DOI":"10.1117\/12.2681650"},{"issue":"1","key":"1419_CR35","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1186\/s13007-021-00767-w","volume":"17","author":"R Zhang","year":"2021","unstructured":"Zhang R, Tian Y, Zhang J, Dai S, Hou X, Wang J, Guo Q (2021) Metric learning for image-based flower cultivars identification. Plant Methods 17(1):65","journal-title":"Plant Methods"},{"key":"1419_CR36","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1419_CR37","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"1419_CR38","unstructured":"Vaswani A (2017) Attention is all you need. Adv Neural Inform Process Syst"},{"key":"1419_CR39","unstructured":"Dosovitskiy A (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"key":"1419_CR40","unstructured":"Touvron H, Cord M, Douze M, Massa F, Sablayrolles A, J\u00e9gou H (2021) Training data-efficient image transformers & distillation through attention. In: International conference on machine learning, pp 10347\u201310357. PMLR"},{"key":"1419_CR41","doi-asserted-by":"crossref","unstructured":"Wu K, Zhang J, Peng H, Liu M, Xiao B, Fu J, Yuan L (2022) Tinyvit: Fast pretraining distillation for small vision transformers. In: European conference on computer vision. Springer, pp 68\u201385","DOI":"10.1007\/978-3-031-19803-8_5"},{"key":"1419_CR42","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"1419_CR43","unstructured":"Vasu PKA, Gabriel J, Zhu J, Tuzel O, Ranjan A (2023) Fastvit: A fast hybrid vision transformer using structural reparameterization. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 5785\u20135795"},{"key":"1419_CR44","doi-asserted-by":"crossref","unstructured":"Liu X, Peng H, Zheng N, Yang Y, Hu H, Yuan Y (2023) Efficientvit: Memory efficient vision transformer with cascaded group attention. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 14420\u201314430","DOI":"10.1109\/CVPR52729.2023.01386"},{"key":"1419_CR45","doi-asserted-by":"crossref","unstructured":"Wang A, Chen H, Lin Z, Han J, Ding G (2024) Repvit: Revisiting mobile cnn from vit perspective. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 15909\u201315920","DOI":"10.1109\/CVPR52733.2024.01506"},{"key":"1419_CR46","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/TMM.2021.3120873","volume":"25","author":"X Lin","year":"2021","unstructured":"Lin X, Sun S, Huang W, Sheng B, Li P, Feng DD (2021) Eapt: efficient attention pyramid transformer for image processing. IEEE Trans Multimedia 25:50\u201361","journal-title":"IEEE Trans Multimedia"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-025-01419-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10044-025-01419-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-025-01419-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T16:38:13Z","timestamp":1751474293000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10044-025-01419-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,11]]},"references-count":46,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1419"],"URL":"https:\/\/doi.org\/10.1007\/s10044-025-01419-8","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"value":"1433-7541","type":"print"},{"value":"1433-755X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,11]]},"assertion":[{"value":"7 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have declared that there are no Conflict of interest exist.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The authors have no Conflict of interest to disclose. Further, the authors certify that, the research presented in this article does not involve any human participants or animals. Moreover, all the datasets used in our experiment are public.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving human and\/or animal rights"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"41"}}