{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:23:40Z","timestamp":1772119420252,"version":"3.50.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"17-18","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s00500-025-10884-6","type":"journal-article","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T09:46:32Z","timestamp":1757929592000},"page":"5327-5340","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Few-shot learning for plant disease detection using DeepBDC"],"prefix":"10.1007","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2513-765X","authenticated-orcid":false,"given":"Rakesh","family":"Ranjan","sequence":"first","affiliation":[]},{"given":"Jyoti Prakash","family":"Singh","sequence":"additional","affiliation":[]},{"given":"Ankit Kumar","family":"Titoriya","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,15]]},"reference":[{"key":"10884_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105542","volume":"175","author":"D Arg\u00fceso","year":"2020","unstructured":"Arg\u00fceso D, Picon A, Irusta U, Medela A, San-Emeterio MG, Bereciartua A, Alvarez-Gila A (2020) Few-shot learning approach for plant disease classification using images taken in the field. Comput Electron Agric 175:105542","journal-title":"Comput Electron Agric"},{"key":"10884_CR2","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.compag.2018.01.009","volume":"145","author":"KP Ferentinos","year":"2018","unstructured":"Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311\u2013318","journal-title":"Comput Electron Agric"},{"key":"10884_CR3","unstructured":"Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp. 1126\u20131135. PMLR"},{"issue":"15","key":"10884_CR4","doi-asserted-by":"publisher","first-page":"23797","DOI":"10.1007\/s11042-023-14372-7","volume":"82","author":"S Garg","year":"2023","unstructured":"Garg S, Singh P (2023) An aggregated loss function based lightweight few-shot model for plant leaf disease classification. Multimed Tools Appl 82(15):23797\u201323815","journal-title":"Multimed Tools Appl"},{"issue":"4","key":"10884_CR5","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1214\/09-AOAS312E","volume":"3","author":"A Gretton","year":"2009","unstructured":"Gretton A, Fukumizu K, Sriperumbudur BK (2009) Discussion of: brownian distance covariance. Ann Appl Stat 3(4):1285\u20131294","journal-title":"Ann Appl Stat"},{"key":"10884_CR6","doi-asserted-by":"crossref","unstructured":"Hao F, He F, Liu L, Wu F, Tao D, Cheng J (2023) Class-aware patch embedding adaptation for few-shot image classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 18905\u201318915","DOI":"10.1109\/ICCV51070.2023.01733"},{"key":"10884_CR7","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":"10884_CR8","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","volume":"147","author":"A Kamilaris","year":"2018","unstructured":"Kamilaris A, Prenafeta-Bold\u00fa FX (2018) Deep learning in agriculture: a survey. Comput Electron Agric 147:70\u201390","journal-title":"Comput Electron Agric"},{"key":"10884_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13007-021-00770-1","volume":"17","author":"Y Li","year":"2021","unstructured":"Li Y, Chao X (2021) Semi-supervised few-shot learning approach for plant diseases recognition. Plant Methods 17:1\u201310","journal-title":"Plant Methods"},{"key":"10884_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106055","volume":"182","author":"Y Li","year":"2021","unstructured":"Li Y, Yang J (2021) Meta-learning baselines and database for few-shot classification in agriculture. Comput Electron Agric 182:106055","journal-title":"Comput Electron Agric"},{"key":"10884_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13007-021-00813-7","volume":"17","author":"X Liang","year":"2021","unstructured":"Liang X (2021) Few-shot cotton leaf spots disease classification based on metric learning. Plant Methods 17:1\u201311","journal-title":"Plant Methods"},{"key":"10884_CR12","unstructured":"Li Z, Zhou F, Chen F, Li H (2017) Meta-SGD: Learning to learn quickly for few-shot learning. arXiv preprint arXiv:1707.09835"},{"issue":"2","key":"10884_CR13","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1094\/PDIS-03-15-0340-FE","volume":"100","author":"A-K Mahlein","year":"2016","unstructured":"Mahlein A-K (2016) Plant disease detection by imaging sensors\u2013parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis 100(2):241\u2013251","journal-title":"Plant Dis"},{"key":"10884_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2023.109306","volume":"49","author":"PK Mensah","year":"2023","unstructured":"Mensah PK, Akoto-Adjepong V, Adu K, Ayidzoe MA, Bediako EA, Nyarko-Boateng O, Amu-Mensah F (2023) CCMT: Dataset for crop pest and disease detection. Data Brief 49:109306","journal-title":"Data Brief"},{"key":"10884_CR15","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2016.01419","volume":"7","author":"SP Mohanty","year":"2016","unstructured":"Mohanty SP, Hughes DP, Salath\u00e9 M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:215232","journal-title":"Front Plant Sci"},{"key":"10884_CR16","unstructured":"Nichol A, Schulman J (2018) Reptile: A scalable metalearning algorithm. arXiv preprint arXiv:1803.02999, 2(3):4"},{"key":"10884_CR17","doi-asserted-by":"crossref","unstructured":"Nuthalapati SV, Tunga A (2021) Multi-domain few-shot learning and dataset for agricultural applications. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1399\u20131408","DOI":"10.1109\/ICCVW54120.2021.00161"},{"issue":"3","key":"10884_CR18","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1007\/s41348-022-00585-9","volume":"129","author":"J Pan","year":"2022","unstructured":"Pan J, Wu Q, Chen Y, Guo Y, Zhao Z (2022) Identification of monocotyledons and dicotyledons leaves diseases with limited multi-category data by few-shot learning. J Plant Dis Prot 129(3):651\u2013663","journal-title":"J Plant Dis Prot"},{"key":"10884_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.mex.2025.103172","volume":"14","author":"R Rani","year":"2025","unstructured":"Rani R, Sahoo J, Bellamkonda S, Kumar S (2025) Attention-enhanced corn disease diagnosis using few-shot learning and VGG16. MethodsX 14:103172","journal-title":"MethodsX"},{"key":"10884_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2024.108812","volume":"219","author":"M Rezaei","year":"2024","unstructured":"Rezaei M, Diepeveen D, Laga H, Jones MG, Sohel F (2024) Plant disease recognition in a low data scenario using few-shot learning. Comput Electron Agric 219:108812","journal-title":"Comput Electron Agric"},{"key":"10884_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2024.109751","volume":"229","author":"M Rezaei","year":"2025","unstructured":"Rezaei M, Diepeveen D, Laga H, Gupta S, Jones MG, Sohel F (2025) A transformer-based few-shot learning pipeline for barley disease detection from field-collected imagery. Comput Electron Agric 229:109751","journal-title":"Comput Electron Agric"},{"key":"10884_CR22","unstructured":"Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. Advances in Neural Information Processing Systems, 30"},{"key":"10884_CR23","doi-asserted-by":"crossref","unstructured":"Sung F, Yang Y, Zhang L, Xiang T, Torr PH, Hospedales TM (2018) Learning to compare: Relation network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1199\u20131208","DOI":"10.1109\/CVPR.2018.00131"},{"key":"10884_CR24","first-page":"55","volume":"6","author":"LM Tassis","year":"2022","unstructured":"Tassis LM, Krohling RA (2022) Few-shot learning for biotic stress classification of coffee leaves. Artif Intell Agric 6:55\u201367","journal-title":"Artif Intell Agric"},{"key":"10884_CR25","doi-asserted-by":"crossref","unstructured":"Tian Y, Wang Y, Krishnan D, Tenenbaum JB, Isola P (2020) Rethinking few-shot image classification: a good embedding is all you need? In: ECCV 2020: Proceedings of the 16th European Conference on Computer Vision, Part XIV, pp. 266\u2013282. Springer,","DOI":"10.1007\/978-3-030-58568-6_16"},{"key":"10884_CR26","doi-asserted-by":"crossref","unstructured":"Titoriya AK, Singh MP, Singh AK (2024) MTUNet++: Explainable few-shot medical image classification with generative adversarial network. Multimedia Tools and Applications, 1\u201321","DOI":"10.1007\/s11042-024-19316-3"},{"issue":"20","key":"10884_CR27","doi-asserted-by":"publisher","first-page":"58293","DOI":"10.1007\/s11042-023-17824-2","volume":"83","author":"P Uskaner Hepsa\u011f","year":"2024","unstructured":"Uskaner Hepsa\u011f P (2024) Efficient plant disease identification using few-shot learning: a transfer learning approach. Multimed Tools Appl 83(20):58293\u201358308","journal-title":"Multimed Tools Appl"},{"key":"10884_CR28","unstructured":"Vinyals O, Blundell C, Lillicrap T, Wierstra D(2016) Matching networks for one shot learning. Advances in Neural Information Processing Systems, 29"},{"issue":"9","key":"10884_CR29","doi-asserted-by":"publisher","first-page":"10956","DOI":"10.1007\/s10489-022-04072-4","volume":"53","author":"B Wang","year":"2023","unstructured":"Wang B, Li L, Verma M, Nakashima Y, Kawasaki R, Nagahara H (2023) Match them up: visually explainable few-shot image classification. Appl Intell 53(9):10956\u201310977","journal-title":"Appl Intell"},{"key":"10884_CR30","unstructured":"Wang Y, Chao WL, Weinberger KQ, Van Der Maaten L (2019) Simpleshot: Revisiting nearest-neighbor classification for few-shot learning. arXiv preprint arXiv:1911.04623"},{"key":"10884_CR31","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY, 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":"10884_CR32","doi-asserted-by":"crossref","unstructured":"Xie J, Long F, Lv J, Wang Q, Li P (2022) Joint distribution matters: Deep brownian distance covariance for few-shot classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7972\u20137981","DOI":"10.1109\/CVPR52688.2022.00781"},{"key":"10884_CR33","doi-asserted-by":"crossref","unstructured":"Ye HJ, Hu H, Zhan DC, Sha F (2020) Few-shot learning via embedding adaptation with set-to-set functions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8808\u20138817","DOI":"10.1109\/CVPR42600.2020.00883"},{"key":"10884_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2024.109498","volume":"227","author":"HT Yuan","year":"2024","unstructured":"Yuan HT, Huang KK, Duan JL, Lai LQ, Yu JX, Huang CW, Yang Z (2024) Generalized few-shot learning for crop hyperspectral image precise classification. Comput Electron Agric 227:109498","journal-title":"Comput Electron Agric"},{"issue":"18","key":"10884_CR35","doi-asserted-by":"publisher","first-page":"54147","DOI":"10.1007\/s11042-023-17136-5","volume":"83","author":"D Zabihzadeh","year":"2024","unstructured":"Zabihzadeh D, Masoudifar M (2024) Zs-dml: zero-shot deep metric learning approach for plant leaf disease classification. Multimed Tools Appl 83(18):54147\u201354164","journal-title":"Multimed Tools Appl"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-025-10884-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-025-10884-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-025-10884-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T08:38:52Z","timestamp":1759221532000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-025-10884-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":35,"journal-issue":{"issue":"17-18","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["10884"],"URL":"https:\/\/doi.org\/10.1007\/s00500-025-10884-6","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-6569821\/v1","asserted-by":"object"}]},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9]]},"assertion":[{"value":"1 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 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 declare no competing interests regarding this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"No requirement for informed consent for this study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Publish"}}]}}