{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T13:46:35Z","timestamp":1776001595899,"version":"3.50.1"},"reference-count":100,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T00:00:00Z","timestamp":1775952000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T00:00:00Z","timestamp":1775952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001791","name":"Griffith University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001791","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-026-21554-6","type":"journal-article","created":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T11:14:55Z","timestamp":1775992495000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MFDP-LeafNet: A few-shot learning method for plant species classification using multi-feature and historical dynamic prototypical network"],"prefix":"10.1007","volume":"85","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3632-937X","authenticated-orcid":false,"given":"Md Ismail","family":"Hossen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Awrangjeb","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shirui","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Aminul","family":"Islam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdullah Al","family":"Mamun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,12]]},"reference":[{"key":"21554_CR1","doi-asserted-by":"crossref","unstructured":"Abdallah HB, Henry CJ, Ramanna S (2024) Plant species recognition with optimized 3d polynomial neural networks and variably overlapping time\u2013coherent sliding window. Multimedia Tools and Applications, pp 1\u201334","DOI":"10.1007\/s11042-024-18480-w"},{"key":"21554_CR2","doi-asserted-by":"crossref","unstructured":"Afrasiyabi A, Lalonde JF, Gagn\u00e9 C (2021) Mixture-based feature space learning for few-shot image classification. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 9041\u20139051","DOI":"10.1109\/ICCV48922.2021.00891"},{"issue":"2","key":"21554_CR3","doi-asserted-by":"publisher","first-page":"2693","DOI":"10.1007\/s40808-023-01918-9","volume":"10","author":"E Amri","year":"2024","unstructured":"Amri E, Gulzar Y, Yeafi A et al (2024) Advancing automatic plant classification system in saudi arabia: introducing a novel dataset and ensemble deep learning approach. Model Earth Syst Environ 10(2):2693\u20132709","journal-title":"Model Earth Syst Environ"},{"key":"21554_CR4","doi-asserted-by":"crossref","unstructured":"Arya A, Agarwal A, Kumar A et al (2023) Plant species identification based on plant leaf using machine learning techniques. In: 2023 4th International Conference on Intelligent Engineering and Management (ICIEM), IEEE, pp 1\u20134","DOI":"10.1109\/ICIEM59379.2023.10166735"},{"key":"21554_CR5","doi-asserted-by":"crossref","unstructured":"Askari F, Fateh A, Mohammadi MR (2024) Enhancing few-shot image classification through learnable multi-scale embedding and attention mechanisms","DOI":"10.1016\/j.neunet.2025.107339"},{"key":"21554_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2022.08.070","volume":"509","author":"J Atanbori","year":"2022","unstructured":"Atanbori J, Rose S (2022) Mergednet: A simple approach for one-shot learning in siamese networks based on similarity layers. Neurocomputing 509:1\u201310","journal-title":"Neurocomputing"},{"issue":"4","key":"21554_CR7","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1007\/s10669-020-09769-w","volume":"40","author":"D Bambil","year":"2020","unstructured":"Bambil D, Pistori H, Bao F et al (2020) Plant species identification using color learning resources, shape, texture, through machine learning and artificial neural networks. Environment Systems and Decisions 40(4):480\u2013484","journal-title":"Environment Systems and Decisions"},{"issue":"3","key":"21554_CR8","first-page":"388","volume":"15","author":"A Bastani","year":"2019","unstructured":"Bastani A, Motee N (2019) A comparative study of machine learning algorithms for plant classification. J Comput Sci 15(3):388\u2013396","journal-title":"J Comput Sci"},{"issue":"7","key":"21554_CR9","doi-asserted-by":"publisher","first-page":"179","DOI":"10.3390\/jimaging8070179","volume":"8","author":"Y Bendou","year":"2022","unstructured":"Bendou Y, Hu Y, Lafargue R et al (2022) Easy\u2013ensemble augmented-shot-y-shaped learning: State-of-the-art few-shot classification with simple components. J Imaging 8(7):179","journal-title":"J Imaging"},{"issue":"4","key":"21554_CR10","doi-asserted-by":"publisher","first-page":"6443","DOI":"10.1007\/s11042-020-10038-w","volume":"80","author":"D Bisen","year":"2021","unstructured":"Bisen D (2021) Deep convolutional neural network based plant species recognition through features of leaf. Multimedia Tools Appl 80(4):6443\u20136456","journal-title":"Multimedia Tools Appl"},{"key":"21554_CR11","doi-asserted-by":"crossref","unstructured":"Bromley J, Guyon I, LeCun Y et al (1993) Signature verification using a\" siamese\" time delay neural network. Adv Neural Inf Process Sys 6","DOI":"10.1142\/9789812797926_0003"},{"key":"21554_CR12","doi-asserted-by":"crossref","unstructured":"Cao W, Zeng J, Liu Q (2025) Flcl: Feature-level contrastive learning for few-shot image classification. IEEE Trans Emerg Topics Comput","DOI":"10.1109\/TETC.2025.3546366"},{"issue":"11","key":"21554_CR13","doi-asserted-by":"publisher","first-page":"210102","DOI":"10.1007\/s11432-022-3700-8","volume":"66","author":"H Chen","year":"2023","unstructured":"Chen H, Li H, Li Y et al (2023) Sparse spatial transformers for few-shot learning. Sci China Inf Sci 66(11):210102","journal-title":"Sci China Inf Sci"},{"issue":"5","key":"21554_CR14","doi-asserted-by":"publisher","first-page":"2389","DOI":"10.1109\/TIP.2018.2886758","volume":"28","author":"T Chen","year":"2018","unstructured":"Chen T, Lu S, Fan J (2018) Ss-hcnn: Semi-supervised hierarchical convolutional neural network for image classification. IEEE Trans Image Process 28(5):2389\u20132398","journal-title":"IEEE Trans Image Process"},{"key":"21554_CR15","unstructured":"Chen WY, Liu YC, Kira Z et al (2019) A closer look at few-shot classification. arXiv:1904.04232 [cs.CV]"},{"key":"21554_CR16","doi-asserted-by":"crossref","unstructured":"Chen Y, Liu Z, Xu H, et al (2021) Meta-Baseline: Exploring simple meta-learning for few-shot learning. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 9062\u20139071","DOI":"10.1109\/ICCV48922.2021.00893"},{"key":"21554_CR17","doi-asserted-by":"crossref","unstructured":"Cheng K, Yang C, Liu X et al (2024) Lpn: Language-guided prototypical network for few-shot classification. IEEE Trans Circ Syst Video Technol","DOI":"10.1109\/TCSVT.2024.3456127"},{"key":"21554_CR18","unstructured":"Choi S (2015) Plant identification with deep convolutional neural network: Snumedinfo at lifeclef plant identification task 2015. CLEF (Working Notes) 2015"},{"key":"21554_CR19","unstructured":"Dhillon GS, Chaudhari P, Ravichandran A et al (2019) A baseline for few-shot image classification. arXiv:1909.02729"},{"key":"21554_CR20","doi-asserted-by":"crossref","unstructured":"Dong C, Li W, Huo J et al (2021) Learning task-aware local representations for few-shot learning. In: Proceedings of the 29th international conference on international joint conferences on artificial intelligence, pp 716\u2013722","DOI":"10.24963\/ijcai.2020\/100"},{"issue":"8","key":"21554_CR21","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1007\/s10489-025-06581-4","volume":"55","author":"M Dong","year":"2025","unstructured":"Dong M, Li F, Li Z et al (2025) Dfpn: a dynamic fusion prototypical network for few-shot learning. Appl Intell 55(8):729","journal-title":"Appl Intell"},{"key":"21554_CR22","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.neucom.2014.10.113","volume":"188","author":"JX Du","year":"2016","unstructured":"Du JX, Shao MW, Zhai CM et al (2016) Recognition of leaf image set based on manifold-manifold distance. Neurocomputing 188:131\u2013138","journal-title":"Neurocomputing"},{"key":"21554_CR23","doi-asserted-by":"crossref","unstructured":"Du R, Chang D, Bhunia AK et al (2020) Fine-grained visual classification via progressive multi-granularity training of jigsaw patches. In: European conference on computer vision, Springer, pp 153\u2013168","DOI":"10.1007\/978-3-030-58565-5_10"},{"key":"21554_CR24","doi-asserted-by":"crossref","unstructured":"Elhariri E, El-Bendary N, Hassanien AE (2014) Plant classification system based on leaf features. In: 2014 9th International Conference on Computer Engineering & Systems (ICCES), IEEE, pp 271\u2013276","DOI":"10.1109\/ICCES.2014.7030971"},{"key":"21554_CR25","unstructured":"Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: International conference on machine learning, PMLR, pp 1126\u20131135"},{"issue":"9","key":"21554_CR26","doi-asserted-by":"publisher","first-page":"2495","DOI":"10.1007\/s13042-022-01539-1","volume":"13","author":"F Gao","year":"2022","unstructured":"Gao F, Cai L, Yang Z et al (2022) Multi-distance metric network for few-shot learning. Int J Mach Learn Cybern 13(9):2495\u20132506","journal-title":"Int J Mach Learn Cybern"},{"key":"21554_CR27","doi-asserted-by":"crossref","unstructured":"Gao T, Han X, Liu Z et al (2019) Hybrid attention-based prototypical networks for noisy few-shot relation classification. In: Proceedings of the AAAI conference on artificial intelligence, pp 6407\u20136414","DOI":"10.1609\/aaai.v33i01.33016407"},{"issue":"3","key":"21554_CR28","doi-asserted-by":"publisher","first-page":"352","DOI":"10.3390\/diagnostics15030352","volume":"15","author":"P Georgiadis","year":"2025","unstructured":"Georgiadis P, Gkouvrikos EV, Vrochidou E et al (2025) Building better deep learning models through dataset fusion: A case study in skin cancer classification with hyperdatasets. Diagnostics 15(3):352","journal-title":"Diagnostics"},{"key":"21554_CR29","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.neucom.2017.01.018","volume":"235","author":"MM Ghazi","year":"2017","unstructured":"Ghazi MM, Yanikoglu B, Aptoula E (2017) Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing 235:228\u2013235","journal-title":"Neurocomputing"},{"key":"21554_CR30","doi-asserted-by":"crossref","unstructured":"Gunning D, Stefik M, Choi J et al (2019) Xai\u2014explainable artificial intelligence. Science robotics 4(37):eaay7120","DOI":"10.1126\/scirobotics.aay7120"},{"key":"21554_CR31","doi-asserted-by":"crossref","unstructured":"Guo M, Wang J, Xu Q et al (2026) Entropy calibrated prototype embedding for transductive few-shot learning. Patt Recogn Lett","DOI":"10.1016\/j.patrec.2026.01.015"},{"key":"21554_CR32","doi-asserted-by":"crossref","unstructured":"Hang ST, Aono M (2016) Open world plant image identification based on convolutional neural network. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, pp 1\u20134","DOI":"10.1109\/APSIPA.2016.7820676"},{"key":"21554_CR33","doi-asserted-by":"crossref","unstructured":"He A, Tian X (2016) Multi-organ plant identification with multi-column deep convolutional neural networks. In: 2016 IEEE international conference on systems, man, and cybernetics (SMC), IEEE, pp 002020\u2013002025","DOI":"10.1109\/SMC.2016.7844537"},{"key":"21554_CR34","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S et al (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":"21554_CR35","doi-asserted-by":"crossref","unstructured":"Hoffer E, Ailon N (2015) Deep metric learning using triplet network. In: Similarity-based pattern recognition: 3rd international workshop, SIMBAD 2015, Copenhagen, Denmark, October 12-14, 2015. Proceedings 3, Springer, pp 84\u201392","DOI":"10.1007\/978-3-319-24261-3_7"},{"key":"21554_CR36","doi-asserted-by":"publisher","first-page":"62307","DOI":"10.1109\/ACCESS.2023.3286730","volume":"11","author":"KM Hosny","year":"2023","unstructured":"Hosny KM, El-Hady WM, Samy FM et al (2023) Multi-class classification of plant leaf diseases using feature fusion of deep convolutional neural network and local binary pattern. IEEE Access 11:62307\u201362317","journal-title":"IEEE Access"},{"key":"21554_CR37","unstructured":"Hou R, Chang H, MA B et al (2019) Cross attention network for few-shot classification. In: Advances in neural information processing systems, vol 32. Curran Associates, Inc"},{"key":"21554_CR38","doi-asserted-by":"crossref","unstructured":"Hu Y, Liu X, Zhang B et al (2021) Alignment enhancement network for fine-grained\u00a1? brk?\u00bf visual categorization. ACM Trans Multimedia Comput Commun Appl (TOMM) 17(1s):1\u201320","DOI":"10.1145\/3446208"},{"key":"21554_CR39","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L et al (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"},{"key":"21554_CR40","unstructured":"Hughes D, Salath\u00e9 M et al (2015) An open access repository of images on plant health to enable the development of mobile disease diagnostics"},{"issue":"1","key":"21554_CR41","first-page":"26","volume":"33","author":"MA Islam","year":"2019","unstructured":"Islam MA, Yousuf MSI, Billah M (2019) Automatic plant detection using hog and lbp features with svm. Int J Comput 33(1):26\u201338","journal-title":"Int J Comput"},{"issue":"25","key":"21554_CR42","doi-asserted-by":"publisher","first-page":"39481","DOI":"10.1007\/s11042-023-14914-z","volume":"82","author":"MA Islam","year":"2023","unstructured":"Islam MA, Hassan MR, Uddin M et al (2023) Germinative paddy seed identification using deep convolutional neural network. Multimedia Tools Appl 82(25):39481\u201339501","journal-title":"Multimedia Tools Appl"},{"issue":"6","key":"21554_CR43","doi-asserted-by":"publisher","first-page":"5609","DOI":"10.1007\/s00500-023-09358-4","volume":"28","author":"SB Jadhav","year":"2024","unstructured":"Jadhav SB, Patil SB (2024) Plant leaf species identification using lbhpg feature extraction and machine learning classifier technique. Soft Comput 28(6):5609\u20135623","journal-title":"Soft Comput"},{"key":"21554_CR44","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.neucom.2019.09.062","volume":"373","author":"Z Ji","year":"2020","unstructured":"Ji Z, Wang H, Pang Y et al (2020) Dual triplet network for image zero-shot learning. Neurocomputing 373:90\u201397","journal-title":"Neurocomputing"},{"key":"21554_CR45","doi-asserted-by":"publisher","first-page":"162590","DOI":"10.1109\/ACCESS.2021.3131726","volume":"9","author":"PS Kanda","year":"2021","unstructured":"Kanda PS, Xia K, Sanusi OH (2021) A deep learning-based recognition technique for plant leaf classification. IEEE Access 9:162590\u2013162613","journal-title":"IEEE Access"},{"key":"21554_CR46","unstructured":"Koch G, Zemel R, Salakhutdinov R et al (2015) Siamese neural networks for one-shot image recognition. In: ICML deep learning workshop, Lille, pp 1\u201330"},{"key":"21554_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.110425","volume":"188","author":"M Koklu","year":"2022","unstructured":"Koklu M, Unlersen MF, Ozkan IA et al (2022) A cnn-svm study based on selected deep features for grapevine leaves classification. Measurement 188:110425","journal-title":"Measurement"},{"key":"21554_CR48","doi-asserted-by":"crossref","unstructured":"Lee K, Maji S, Ravichandran A et al (2019) Meta-learning with differentiable convex optimization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10657\u201310665","DOI":"10.1109\/CVPR.2019.01091"},{"key":"21554_CR49","doi-asserted-by":"crossref","unstructured":"Li W, Wang L, Xu J et al (2019) Revisiting local descriptor based image-to-class measure for few-shot learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7260\u20137268","DOI":"10.1109\/CVPR.2019.00743"},{"key":"21554_CR50","doi-asserted-by":"crossref","unstructured":"Li W, Wang L, Huo J et al (2020) Asymmetric distribution measure for few-shot learning","DOI":"10.24963\/ijcai.2020\/409"},{"key":"21554_CR51","doi-asserted-by":"crossref","unstructured":"Lifchitz Y, Avrithis Y, Picard S et al (2019) Dense classification and implanting for few-shot learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9258\u20139267","DOI":"10.1109\/CVPR.2019.00948"},{"issue":"21","key":"21554_CR52","doi-asserted-by":"publisher","first-page":"2814","DOI":"10.3390\/plants11212814","volume":"11","author":"H Lin","year":"2022","unstructured":"Lin H, Tse R, Tang SK et al (2022) Few-shot learning for plant-disease recognition in the frequency domain. Plants 11(21):2814","journal-title":"Plants"},{"key":"21554_CR53","doi-asserted-by":"crossref","unstructured":"Liu B, Cao Y, Lin Y et al (2020) Negative margin matters: Understanding margin in few-shot classification. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part IV 16, Springer, pp 438\u2013455","DOI":"10.1007\/978-3-030-58548-8_26"},{"issue":"2","key":"21554_CR54","doi-asserted-by":"publisher","first-page":"1278","DOI":"10.1109\/TCBB.2022.3195291","volume":"20","author":"K Liu","year":"2023","unstructured":"Liu K, Zhang X (2023) Pitlid: Identification of plant disease from leaf images based on convolutional neural network. IEEE\/ACM Trans Comput Biol Bioinf 20(2):1278\u20131288","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"21554_CR55","first-page":"9373210","volume":"1","author":"X Liu","year":"2018","unstructured":"Liu X, Xu F, Sun Y et al (2018) (2018) Convolutional recurrent neural networks for observation-centered plant identification. J Electr Comput Eng 1:9373210","journal-title":"J Electr Comput Eng"},{"issue":"4","key":"21554_CR56","first-page":"1465","volume":"29","author":"L Longlong","year":"2015","unstructured":"Longlong L, Garibaldi JM, Dongjian H (2015) Leaf classification using multiple feature analysis based on semi-supervised clustering. J Intell Fuzzy Syst 29(4):1465\u20131477","journal-title":"J Intell Fuzzy Syst"},{"key":"21554_CR57","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2023.103907","volume":"134","author":"Z Lv","year":"2023","unstructured":"Lv Z, Zhang Z (2023) Research on plant leaf recognition method based on multi-feature fusion in different partition blocks. Digital Signal Processing 134:103907","journal-title":"Digital Signal Processing"},{"key":"21554_CR58","doi-asserted-by":"crossref","unstructured":"Muthevi A, Uppu RB (2017) Leaf classification using completed local binary pattern of textures. In: 2017 IEEE 7th International Advance Computing Conference (IACC), IEEE, pp 870\u2013874","DOI":"10.1109\/IACC.2017.0178"},{"key":"21554_CR59","doi-asserted-by":"crossref","unstructured":"Nayem J, Hasan SS, Amina N et al (2023) Few shot learning for medical imaging: A comparative analysis of methodologies and formal mathematical framework. In: Data driven approaches on medical imaging. Springer, pp 69\u201390","DOI":"10.1007\/978-3-031-47772-0_4"},{"issue":"2","key":"21554_CR60","first-page":"1185","volume":"3","author":"P Nidheesh","year":"2015","unstructured":"Nidheesh P, Rajeev A, Nikesh P (2015) Classification of leaf using geometric features. Int J Eng Res Gen Sci 3(2):1185\u20131190","journal-title":"Int J Eng Res Gen Sci"},{"key":"21554_CR61","doi-asserted-by":"publisher","DOI":"10.2196\/44293","volume":"2","author":"D Oniani","year":"2023","unstructured":"Oniani D, Chandrasekar P, Sivarajkumar S et al (2023) Few-shot learning for clinical natural language processing using siamese neural networks: Algorithm development and validation study. JMIR AI 2:e44293","journal-title":"JMIR AI"},{"key":"21554_CR62","unstructured":"Oreshkin B, Rodr\u00edguez L\u00f3pez P, Lacoste A (2018) Tadam: Task dependent adaptive metric for improved few-shot learning. Adv Neural Inf Process Syst 31"},{"key":"21554_CR63","unstructured":"Oreshkin B, L\u00f3pez PR, Tadam AL (2020) Task dependent adaptive metric for improved few-shot learning. In: International conference on neural information processing systems, pp 721\u2013731"},{"key":"21554_CR64","doi-asserted-by":"crossref","unstructured":"Padmanabhan DC, Gowda S, Arani E et al (2023) Lsfsl: Leveraging shape information in few-shot learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4971\u20134980","DOI":"10.1109\/CVPRW59228.2023.00525"},{"key":"21554_CR65","unstructured":"Raghu A, Raghu M, Bengio S et al (2019) Rapid learning or feature reuse?towards understanding the effectiveness of maml. arXiv preprint arXiv:1909.09157"},{"key":"21554_CR66","doi-asserted-by":"crossref","unstructured":"Sajitha P, Andrushia AD, Anand N et al (2024) A review on machine learning and deep learning image-based plant disease classification for industrial farming systems. J Ind Inf Integr, p 100572","DOI":"10.1016\/j.jii.2024.100572"},{"key":"21554_CR67","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"21554_CR68","unstructured":"Santoro A, Raposo D, Barrett DG et al (2017) A simple neural network module for relational reasoning. In: Advances in neural information processing systems, vol 30. Curran Associates, Inc"},{"key":"21554_CR69","doi-asserted-by":"crossref","unstructured":"Sarhan AM, Shaheen MA (2026) Egypli: A real-life annotated image dataset for egyptian plant leaf identification. Scientific Data","DOI":"10.1038\/s41597-025-06539-8"},{"key":"21554_CR70","volume-title":"Advances in Neural Information Processing Systems","author":"J Snell","year":"2017","unstructured":"Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. In: Guyon I, Luxburg UV, Bengio S et al (eds) Advances in Neural Information Processing Systems, vol 30. Curran Associates Inc"},{"key":"21554_CR71","unstructured":"S\u00f6derkvist O (2001) Computer vision classification of leaves from swedish trees"},{"key":"21554_CR72","doi-asserted-by":"crossref","unstructured":"Song Z, Qiang W, Zheng C et al (2024) On the discriminability of self-supervised representation learning. arXiv preprint arXiv:2407.13541","DOI":"10.1016\/j.ins.2025.122556"},{"key":"21554_CR73","doi-asserted-by":"crossref","unstructured":"Song Z, Wang P, Wang X et al (2025) Adptgl-ca: Adaptive global-local metric fusion with contrastive attention for few-shot learning. IEEE Trans Emerg Topics Comput Intell","DOI":"10.1109\/TETCI.2025.3550529"},{"key":"21554_CR74","doi-asserted-by":"crossref","unstructured":"Sung F, Yang Y, Zhang L et al (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":"21554_CR75","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S et al (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"21554_CR76","unstructured":"Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning, PMLR, pp 6105\u20136114"},{"key":"21554_CR77","unstructured":"Tang S, Luo G, Ye X et al (2024) Unleash the power of local representations for few-shot classification"},{"key":"21554_CR78","doi-asserted-by":"crossref","unstructured":"Tian Y, Wang Y, Krishnan D et al (2020) Rethinking few-shot image classification: a good embedding is all you need? In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XIV 16, Springer, pp 266\u2013282","DOI":"10.1007\/978-3-030-58568-6_16"},{"key":"21554_CR79","unstructured":"Trabucco B, Doherty K, Gurinas M et al (2023) Effective data augmentation with diffusion models"},{"key":"21554_CR80","unstructured":"Triantafillou E, Zhu T, Dumoulin V et al (2019) Meta-dataset: A dataset of datasets for learning to learn from few examples. arXiv:1903.03096 [cs.LG]"},{"issue":"1","key":"21554_CR81","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1007\/s40747-021-00545-0","volume":"8","author":"M Uddin","year":"2022","unstructured":"Uddin M, Islam MA, Shajalal M et al (2022) Paddy seed variety identification using t20-hog and haralick textural features. Complex & Intelligent Systems 8(1):657\u2013671","journal-title":"Complex & Intelligent Systems"},{"issue":"2","key":"21554_CR82","first-page":"191","volume":"6","author":"N Valliammal","year":"2012","unstructured":"Valliammal N, Geethalakshmi S (2012) An optimal feature subset selection for leaf analysis. Int J Comput Inf Eng 6(2):191\u2013196","journal-title":"Int J Comput Inf Eng"},{"key":"21554_CR83","unstructured":"Vinyals O, Blundell C, Lillicrap T et al (2016) Matching networks for one shot learning. In: Advances in neural information processing systems, vol 29. Curran Associates, Inc"},{"key":"21554_CR84","doi-asserted-by":"publisher","first-page":"151754","DOI":"10.1109\/ACCESS.2019.2947510","volume":"7","author":"B Wang","year":"2019","unstructured":"Wang B, Wang D (2019) Plant leaves classification: A few-shot learning method based on siamese network. Ieee Access 7:151754\u2013151763","journal-title":"Ieee Access"},{"issue":"4","key":"21554_CR85","doi-asserted-by":"publisher","first-page":"10865","DOI":"10.1007\/s11042-023-15892-y","volume":"83","author":"L Wang","year":"2024","unstructured":"Wang L, He K, Liu Z (2024) Mcs: a metric confidence selection framework for few shot image classification. Multimedia Tools and Applications 83(4):10865\u201310880","journal-title":"Multimedia Tools and Applications"},{"issue":"12","key":"21554_CR86","doi-asserted-by":"publisher","first-page":"7789","DOI":"10.1109\/TCSVT.2023.3282777","volume":"33","author":"X Wang","year":"2023","unstructured":"Wang X, Wang X, Jiang B et al (2023) Few-shot learning meets transformer: Unified query-support transformers for few-shot classification. IEEE Trans Circuits Syst Video Technol 33(12):7789\u20137802","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"21554_CR87","unstructured":"Wang Y, Chao WL, Weinberger KQ et al (2019) Simpleshot: Revisiting nearest-neighbour classification for few-shot learning. arXiv:1911.04623"},{"issue":"3","key":"21554_CR88","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3386252","volume":"53","author":"Y Wang","year":"2020","unstructured":"Wang Y, Yao Q, Kwok JT et al (2020) Generalizing from a few examples: A survey on few-shot learning. ACM computing surveys (csur) 53(3):1\u201334","journal-title":"ACM computing surveys (csur)"},{"issue":"1","key":"21554_CR89","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/MSP.2008.930649","volume":"26","author":"Z Wang","year":"2009","unstructured":"Wang Z, Bovik AC (2009) Mean squared error: Love it or leave it? a new look at signal fidelity measures. IEEE Signal Process Mag 26(1):98\u2013117","journal-title":"IEEE Signal Process Mag"},{"key":"21554_CR90","doi-asserted-by":"crossref","unstructured":"Wu C, Yang J, Shang Y et al (2024) Dynamically weighted prototypical learning method for few-shot sar atr. IEEE Geosci Remote Sens Lett","DOI":"10.1109\/LGRS.2024.3365147"},{"key":"21554_CR91","doi-asserted-by":"crossref","unstructured":"Wu J, Yu J, Wang S et al (2025) Prototype completion and dynamic loss in few-shot classification network. Int J Mach Learn Cybernet, pp 1\u201316","DOI":"10.1007\/s13042-025-02738-2"},{"issue":"7","key":"21554_CR92","doi-asserted-by":"publisher","first-page":"2043","DOI":"10.1109\/TNNLS.2018.2876179","volume":"30","author":"S Wu","year":"2018","unstructured":"Wu S, Li G, Deng L et al (2018) $$ l1 $$-norm batch normalization for efficient training of deep neural networks. IEEE Trans Neural Netw Learn Syst 30(7):2043\u20132051","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"21554_CR93","doi-asserted-by":"crossref","unstructured":"Wu SG, Bao FS, Xu EY et al (2007) A leaf recognition algorithm for plant classification using probabilistic neural network. In: 2007 IEEE international symposium on signal processing and information technology, IEEE, pp 11\u201316","DOI":"10.1109\/ISSPIT.2007.4458016"},{"key":"21554_CR94","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107809","volume":"112","author":"C Yang","year":"2021","unstructured":"Yang C (2021) Plant leaf recognition by integrating shape and texture features. Pattern Recogn 112:107809","journal-title":"Pattern Recogn"},{"key":"21554_CR95","doi-asserted-by":"crossref","unstructured":"Ye HJ, Hu H, Zhan DC et al (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"},{"issue":"2","key":"21554_CR96","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.knosys.2010.11.002","volume":"24","author":"S Zhang","year":"2011","unstructured":"Zhang S, Lei YK, Wu YH (2011) Semi-supervised locally discriminant projection for classification and recognition. Knowl-Based Syst 24(2):341\u2013346","journal-title":"Knowl-Based Syst"},{"key":"21554_CR97","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1016\/j.neucom.2019.09.113","volume":"408","author":"S Zhang","year":"2020","unstructured":"Zhang S, Huang W, Ya H et al (2020) Plant species recognition methods using leaf image: Overview. Neurocomputing 408:246\u2013272","journal-title":"Neurocomputing"},{"key":"21554_CR98","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2024.109373","volume":"226","author":"Y Zhao","year":"2024","unstructured":"Zhao Y, Zhang Z, Wu N et al (2024) Mafde-dn4: Improved few-shot plant disease classification method based on deep nearest neighbor neural network. Comput Electron Agric 226:109373","journal-title":"Comput Electron Agric"},{"key":"21554_CR99","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102672","volume":"114","author":"C Zhou","year":"2025","unstructured":"Zhou C, Yu Z, Yuan X et al (2025) Less is more: A closer look at semantic-based few-shot learning. Information Fusion 114:102672","journal-title":"Information Fusion"},{"issue":"1","key":"21554_CR100","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3615659","volume":"20","author":"C Zou","year":"2023","unstructured":"Zou C, Wang R, Jin C et al (2023) S2cl-leaf net: Recognizing leaf images like human botanists. ACM Trans Multimed Comput Commun Appl 20(1):1\u201320","journal-title":"ACM Trans Multimed Comput Commun Appl"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21554-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-026-21554-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21554-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T13:02:07Z","timestamp":1775998927000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-026-21554-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,12]]},"references-count":100,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["21554"],"URL":"https:\/\/doi.org\/10.1007\/s11042-026-21554-6","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,12]]},"assertion":[{"value":"5 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 April 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2026","order":4,"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 conflicts of interest relevant to this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Not Applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}],"article-number":"357"}}