{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T18:49:29Z","timestamp":1770230969246,"version":"3.49.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"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":"publisher","award":["62174121"],"award-info":[{"award-number":["62174121"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LQ23F050002"],"award-info":[{"award-number":["LQ23F050002"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ningbo Public Welfare Science and Technology Project","award":["2022S078"],"award-info":[{"award-number":["2022S078"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s10489-025-06783-w","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T12:51:53Z","timestamp":1754916713000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Diffusion-augmented nematode dataset improves few-shot classification of nematodes"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5835-4418","authenticated-orcid":false,"given":"Ying","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Pengjun","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jiayan","family":"Zhuang","sequence":"additional","affiliation":[]},{"given":"Jiangjian","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Jianfeng","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Weilun","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Xiong","family":"Ouyang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,11]]},"reference":[{"issue":"6","key":"6783_CR1","first-page":"1","volume":"41","author":"K Zhuo","year":"2015","unstructured":"Zhuo K, Liao J (2015) Advances in molecular identification of plant nematodes. Plant Prot 41(6):1\u20138 (in Chinese)","journal-title":"Plant Prot"},{"issue":"1","key":"6783_CR2","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/s10462-023-10631-z","volume":"57","author":"R Archana","year":"2024","unstructured":"Archana R, Jeevaraj PSE (2024) Deep learning models for digital image processing: A review. Artif Intell Rev 57(1):11. https:\/\/doi.org\/10.1007\/s10462-023-10631-z","journal-title":"Artif Intell Rev"},{"key":"6783_CR3","doi-asserted-by":"publisher","first-page":"107710","DOI":"10.1016\/j.compag.2023.107710","volume":"207","author":"Y Zhu","year":"2023","unstructured":"Zhu Y, Zhuang J, Ye S, Xu N, Xiao J, Gu J et al (2023) Domain generalization in nematode classification. Comput Electron Agric 207:107710. https:\/\/doi.org\/10.1016\/j.compag.2023.107710","journal-title":"Comput Electron Agric"},{"key":"6783_CR4","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.biosystemseng.2021.11.016","volume":"213","author":"A Abade","year":"2022","unstructured":"Abade A, Porto LF, Ferreira PA, de Barros Vidal F (2022) Nemanet: A convolutional neural network model for identification of soybean nematodes. Biosyst Eng 213:39\u201362. https:\/\/doi.org\/10.1016\/j.biosystemseng.2021.11.016","journal-title":"Biosyst Eng"},{"key":"6783_CR5","doi-asserted-by":"publisher","first-page":"106058","DOI":"10.1016\/j.compag.2021.106058","volume":"186","author":"R Thevenoux","year":"2021","unstructured":"Thevenoux R, Le VL, Villess\u00e8che H, Buisson A, Beurton-Aimar M, Grenier E et al (2021) Image-based species identification of globodera quarantine nematodes using computer vision and deep learning. Comput Electron Agric 186:106058. https:\/\/doi.org\/10.1016\/j.compag.2021.106058","journal-title":"Comput Electron Agric"},{"key":"6783_CR6","doi-asserted-by":"publisher","first-page":"100258","DOI":"10.1016\/j.array.2022.100258","volume":"16","author":"A Mumuni","year":"2022","unstructured":"Mumuni A, Mumuni F (2022) Data augmentation: A comprehensive survey of modern approaches. Array 16:100258. https:\/\/doi.org\/10.1016\/j.array.2022.100258","journal-title":"Array"},{"key":"6783_CR7","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1312.6114","author":"D P Kingma","year":"2014","unstructured":"P Kingma D, Welling M (2014) Auto-encoding variational Bayes. ArXiv. https:\/\/doi.org\/10.48550\/ArXiv.1312.6114","journal-title":"ArXiv"},{"key":"6783_CR8","doi-asserted-by":"publisher","first-page":"2672","DOI":"10.48550\/arXiv.1406.2661","volume":"27","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S et al (2014) Generative adversarial Nets. Adv Neural Inf Process Syst 27:2672\u20132680. https:\/\/doi.org\/10.48550\/arXiv.1406.2661","journal-title":"Adv Neural Inf Process Syst"},{"key":"6783_CR9","doi-asserted-by":"publisher","unstructured":"Liu B, Zhu Y, Song K et al (2021) Towards faster and stabilized GAN training for high-fidelity few-shot image synthesis. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2101.04775","DOI":"10.48550\/arXiv.2101.04775"},{"key":"6783_CR10","doi-asserted-by":"publisher","unstructured":"Karras T, Aittala M, Laine S (2021) Alias-free generative adversarial networks. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2106.12423","DOI":"10.48550\/arXiv.2106.12423"},{"key":"6783_CR11","doi-asserted-by":"publisher","first-page":"6840","DOI":"10.48550\/arXiv.2006.11239","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho J, Chen X, Ermon S (2020) Denoising diffusion probabilistic models. Adv Neural Inf Process Syst 33:6840\u20136851. https:\/\/doi.org\/10.48550\/arXiv.2006.11239","journal-title":"Adv Neural Inf Process Syst"},{"key":"6783_CR12","doi-asserted-by":"publisher","first-page":"8780","DOI":"10.48550\/arXiv.2105.05233","volume":"34","author":"P Dhariwal","year":"2021","unstructured":"Dhariwal P, Nichol A (2021) Diffusion models beat GANs on image synthesis. Adv Neural Inf Process Syst 34:8780\u20138794. https:\/\/doi.org\/10.48550\/arXiv.2105.05233","journal-title":"Adv Neural Inf Process Syst"},{"key":"6783_CR13","doi-asserted-by":"publisher","unstructured":"Ramesh A, Dhariwal P, Nichol A, Chu C, Chen M (2022) Hierarchical text-conditional image generation with CLIP latents. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2204.06125","DOI":"10.48550\/arXiv.2204.06125"},{"key":"6783_CR14","doi-asserted-by":"publisher","unstructured":"Yang Y, Fu H, Aviles-Rivero AI, Sch\u00f6nlieb CB, Zhu L (2023) DiffMIC: Dual-guidance diffusion network for medical image classification. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2303.10610","DOI":"10.48550\/arXiv.2303.10610"},{"key":"6783_CR15","doi-asserted-by":"publisher","unstructured":"Zhang Z, Yao L, Wang B, Jha D, Keles E, Medetalibeyoglu A, Bagci U (2023) EMIT-Diff: Enhancing medical image segmentation via text-guided diffusion model. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2310.12868","DOI":"10.48550\/arXiv.2310.12868"},{"key":"6783_CR16","doi-asserted-by":"publisher","unstructured":"Dombrowski M, Reynaud H, M\u00fcller JP, Baugh M, Kainz B (2023) Trade-offs in fine-tuned diffusion models between accuracy and interpretability. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2303.17908","DOI":"10.48550\/arXiv.2303.17908"},{"key":"6783_CR17","doi-asserted-by":"publisher","unstructured":"Rombach R, Blattmann A, Lorenz D, Esser P, Ommer B (2022) High-resolution image synthesis with latent diffusion models. Proc IEEE\/CVF Conf Comput Vis Pattern Recognit (CVPR) 10674\u201310685. https:\/\/doi.org\/10.1109\/CVPR52688.2022.01042","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"6783_CR18","doi-asserted-by":"publisher","unstructured":"Trabucco B, Doherty K, Gurinas M, Salakhutdinov R (2023) Effective data augmentation with diffusion models. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2302.07944","DOI":"10.48550\/arXiv.2302.07944"},{"key":"6783_CR19","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2311.05232","author":"L Huang","year":"2023","unstructured":"Huang L, Yu W, Ma W, Zhong W, Feng Z, Wang H, Liu T (2023) A survey on hallucination in large Language models: principles, taxonomy, challenges, and open questions. ArXiv. https:\/\/doi.org\/10.48550\/ArXiv.2311.05232","journal-title":"ArXiv"},{"key":"6783_CR20","doi-asserted-by":"publisher","unstructured":"Gal R, Alaluf Y, Atzmon Y, Patashnik O, Bermano AH, Chechik G, Cohen-Or D (2022) An image is worth one word: personalizing text-to-image generation using textual inversion. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2208.01618","DOI":"10.48550\/arXiv.2208.01618"},{"key":"6783_CR21","doi-asserted-by":"publisher","unstructured":"Zhang L, Rao A, Agrawala M (2023) Adding conditional control to text-to-image diffusion models. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2302.05543","DOI":"10.48550\/arXiv.2302.05543"},{"key":"6783_CR22","doi-asserted-by":"publisher","unstructured":"Hu EJ, Shen Y, Wallis P, Allen-Zhu Z, Li Y, Wang S, Chen W (2021) LoRA: Low-rank adaptation of large language models. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2106.09685","DOI":"10.48550\/arXiv.2106.09685"},{"key":"6783_CR23","doi-asserted-by":"publisher","first-page":"108324","DOI":"10.1016\/j.compag.2023.108324","volume":"214","author":"H Moreno","year":"2023","unstructured":"Moreno H, G\u00f3mez A, Altares-L\u00f3pez S, Ribeiro A, And\u00fajar D (2023) Analysis of stable diffusion-derived fake weeds performance for training convolutional neural networks. Comput Electron Agric 214:108324. https:\/\/doi.org\/10.1016\/j.compag.2023.108324","journal-title":"Comput Electron Agric"},{"key":"6783_CR24","doi-asserted-by":"publisher","unstructured":"Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) GANs trained by a two time-scale update rule converge to a local Nash equilibrium. Adv Neural Inf Process Syst 30. https:\/\/doi.org\/10.48550\/arXiv.1706.08500","DOI":"10.48550\/arXiv.1706.08500"},{"issue":"4","key":"6783_CR25","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612. https:\/\/doi.org\/10.1109\/TIP.2003.819861","journal-title":"IEEE Trans Image Process"},{"key":"6783_CR26","doi-asserted-by":"publisher","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition arXiv. https:\/\/doi.org\/10.48550\/arXiv.1409.1556","DOI":"10.48550\/arXiv.1409.1556"},{"key":"6783_CR27","doi-asserted-by":"publisher","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) MobileNetV2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp 4510\u20134520. https:\/\/doi.org\/10.1109\/CVPR.2018.00474","DOI":"10.1109\/CVPR.2018.00474"},{"key":"6783_CR28","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Proc IEEE Conf Comput Vis Pattern Recognit (CVPR) 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"6783_CR29","doi-asserted-by":"publisher","unstructured":"Huang G, Liu Z, van der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2261\u20132269. https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"6783_CR30","doi-asserted-by":"publisher","unstructured":"Tan M, Le Q (2021) EfficientNetV2: smaller models and faster training. Proc 38th Int Conf Mach Learn (ICML) 10096\u201310106. https:\/\/doi.org\/10.48550\/arXiv.2104.00298","DOI":"10.48550\/arXiv.2104.00298"},{"key":"6783_CR31","doi-asserted-by":"publisher","unstructured":"Fang H, Han B, Zhang S, Zhou S, Hu C, Ye WM (2024) Data augmentation for object detection via controllable diffusion models. Proc IEEE\/CVF Winter Conf Appl Comput Vis (WACV) pp 1246\u20131255. https:\/\/doi.org\/10.1109\/WACV57701.2024.00129","DOI":"10.1109\/WACV57701.2024.00129"},{"key":"6783_CR32","doi-asserted-by":"publisher","unstructured":"Graikos A, Yellapragada S, Le MQ, Kapse S, Prasanna P, Saltz J (2024) Learned representation-guided diffusion models for large-image generation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 8532\u20138542. https:\/\/doi.org\/10.1109\/CVPR52733.2024.00815","DOI":"10.1109\/CVPR52733.2024.00815"},{"key":"6783_CR33","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2210.07574","author":"R He","year":"2023","unstructured":"He R, Sun S, Yu X, Xue C, Zhang W, Torr P, Bai S, Qi X (2023) Is synthetic data from generative models ready for image recognition? Int Conf Learn Representations (ICLR). https:\/\/doi.org\/10.48550\/arXiv.2210.07574","journal-title":"Int Conf Learn Representations (ICLR)"},{"key":"6783_CR34","doi-asserted-by":"publisher","first-page":"108517","DOI":"10.1016\/j.compag.2023.108517","volume":"216","author":"D Chen","year":"2024","unstructured":"Chen D, Qi X, Zheng Y, Lu Y, Huang Y, Li Z (2024) Synthetic data augmentation by diffusion probabilistic models to enhance weed recognition. Comput Electron Agric 216:108517. https:\/\/doi.org\/10.1016\/j.compag.2023.108517","journal-title":"Comput Electron Agric"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06783-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06783-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06783-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T14:31:58Z","timestamp":1758983518000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06783-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8]]},"references-count":34,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["6783"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06783-w","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8]]},"assertion":[{"value":"12 July 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2025","order":2,"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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}],"article-number":"918"}}