{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T21:03:38Z","timestamp":1760821418113,"version":"3.37.3"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"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":["42061067","52063002"],"award-info":[{"award-number":["42061067","52063002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-16602-4","type":"journal-article","created":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T09:02:54Z","timestamp":1693558974000},"page":"27411-27433","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Identification of apple leaf diseases using C-Grabcut algorithm and improved transfer learning base on low shot learning"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4455-3227","authenticated-orcid":false,"given":"Suyun","family":"Lian","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5510-0648","authenticated-orcid":false,"given":"Lixin","family":"Guan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0759-9568","authenticated-orcid":false,"given":"Jihong","family":"Pei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0987-3484","authenticated-orcid":false,"given":"Gui","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7832-4185","authenticated-orcid":false,"given":"Mengshan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,1]]},"reference":[{"unstructured":"PD, S.A(2019)World population prospects 2019: highlights. 2019; Available from: https:\/\/www.un.org\/development\/desa\/publications\/world-population-prospects-2019-highlights.html","key":"16602_CR1"},{"key":"16602_CR2","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.3389\/fpls.2016.01419","volume":"7","author":"SP Mohanty","year":"2016","unstructured":"Mohanty SP, Hughes DP, Salathe M (2016) Using Deep Learning for Image-Based Plant Disease Detection. Front Plant Sci 7:1419. https:\/\/doi.org\/10.3389\/fpls.2016.01419","journal-title":"Front Plant Sci"},{"key":"16602_CR3","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.ecolecon.2013.10.005","volume":"97","author":"IYR Odegard","year":"2014","unstructured":"Odegard IYR, van der Voet E (2014) The future of food - Scenarios and the effect on natural resource use in agriculture in 2050. Ecol Econ 97:51\u201359. https:\/\/doi.org\/10.1016\/j.ecolecon.2013.10.005","journal-title":"Ecol Econ"},{"issue":"10","key":"16602_CR4","doi-asserted-by":"publisher","first-page":"2827","DOI":"10.1093\/jxb\/erp080","volume":"60","author":"PJ Gregory","year":"2009","unstructured":"Gregory PJ et al (2009) Integrating pests and pathogens into the climate change\/food security debate. J Exp Bot 60(10):2827\u20132838. https:\/\/doi.org\/10.1093\/jxb\/erp080","journal-title":"J Exp Bot"},{"issue":"No.4","key":"16602_CR5","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1016\/j.cropro.2005.05.003","volume":"25","author":"H Dong","year":"2006","unstructured":"Dong H et al (2006) Dry mycelium of Penicillium chrysogenum protects cotton plants against wilt diseases and increases yield under field conditions. Crop Protect 25(No.4):324\u2013330. https:\/\/doi.org\/10.1016\/j.cropro.2005.05.003","journal-title":"Crop Protect"},{"doi-asserted-by":"publisher","unstructured":"Braunack MV, Garside AL, Magarey RC (2012) Reduced tillage planting and the long-term effect on soil-borne disease and yield of sugarcane (Saccharum inter-specific hybrid) in Queensland, Australia. Soil Tillage Res, (No.1): 85\u201391. https:\/\/doi.org\/10.1016\/j.still.2011.11.002","key":"16602_CR6","DOI":"10.1016\/j.still.2011.11.002"},{"doi-asserted-by":"publisher","unstructured":"Kaur S, Pandey S, Goel S (2019) Plants Disease Identification and Classification Through Leaf Images: A Survey. Arch Comput Methods Eng, (No.2): 507\u2013530. https:\/\/doi.org\/10.1007\/s11831-018-9255-6","key":"16602_CR7","DOI":"10.1007\/s11831-018-9255-6"},{"key":"16602_CR8","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.procs.2015.08.022","volume":"58","author":"M Bhange","year":"2015","unstructured":"Bhange M, Hingoliwala H (2015) Smart farming: Pomegranate disease detection using image processing. Procedia Comput Sci 58:280\u2013288","journal-title":"Procedia Comput Sci"},{"doi-asserted-by":"crossref","unstructured":"Sabrol H, Satish K (2016) Tomato plant disease classification in digital images using classification tree. in 2016 international conference on communication and signal processing (ICCSP). IEEE","key":"16602_CR9","DOI":"10.1109\/ICCSP.2016.7754351"},{"key":"16602_CR10","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1007\/s11277-017-5092-4","volume":"102","author":"S Aasha Nandhini","year":"2018","unstructured":"Aasha Nandhini S et al (2018) Web enabled plant disease detection system for agricultural applications using WMSN. Wireless Pers Commun 102:725\u2013740","journal-title":"Wireless Pers Commun"},{"issue":"6","key":"16602_CR11","first-page":"16815","volume":"5","author":"SS Panchal","year":"2016","unstructured":"Panchal SS, Sonar R (2016) Pomegranate leaf disease detection using support vector machine. Int J Eng Comput Sci 5(6):16815\u201316818","journal-title":"Int J Eng Comput Sci"},{"doi-asserted-by":"crossref","unstructured":"Qi F, Wang Y, Tang Z (2022) Lightweight plant disease classification combining grabcut algorithm, new coordinate attention, and channel pruning. Neural Process Lett 54(6):5317\u20135331","key":"16602_CR12","DOI":"10.1007\/s11063-022-10863-0"},{"issue":"3","key":"16602_CR13","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1145\/1015706.1015720","volume":"23","author":"C Rother","year":"2004","unstructured":"Rother C, Kolmogorov V, Blake A (2004) \u201c GrabCut\u201d interactive foreground extraction using iterated graph cuts. ACM Trans Graph (TOG) 23(3):309\u2013314","journal-title":"ACM Trans Graph (TOG)"},{"unstructured":"Chen L-C et al (2017) Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587","key":"16602_CR14"},{"doi-asserted-by":"crossref","unstructured":"Douillard A et al (2021) Plop: Learning without forgetting for continual semantic segmentation. in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","key":"16602_CR15","DOI":"10.1109\/CVPR46437.2021.00403"},{"unstructured":"Zhou MM (2019) Apple foliage diseases recognition in Android system with transfer learning-based. MS thesis, Dept Inf Eng, NorthwestA&FNorthwest A&F Univ, Yangling, China","key":"16602_CR16"},{"doi-asserted-by":"crossref","unstructured":"Gao H et al (2022) A mutually supervised graph attention network for few-shot segmentation: the perspective of fully utilizing limited samples. IEEE Transactions on neural networks and learning systems:1\u201313","key":"16602_CR17","DOI":"10.1109\/TNNLS.2022.3155486"},{"unstructured":"Rakelly K et al (2018) Conditional networks for few-shot semantic segmentation","key":"16602_CR18"},{"key":"16602_CR19","doi-asserted-by":"publisher","first-page":"166109","DOI":"10.1109\/ACCESS.2019.2953465","volume":"7","author":"Z Cao","year":"2019","unstructured":"Cao Z et al (2019) Meta-seg: A generalized meta-learning framework for multi-class few-shot semantic segmentation. IEEE Access 7:166109\u2013166121","journal-title":"IEEE Access"},{"key":"16602_CR20","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s42161-020-00683-3","volume":"103","author":"VK Shrivastava","year":"2021","unstructured":"Shrivastava VK, Pradhan MK (2021) Rice plant disease classification using color features: a machine learning paradigm. J Plant Pathol 103:17\u201326","journal-title":"J Plant Pathol"},{"doi-asserted-by":"crossref","unstructured":"Jaisakthi S, Mirunalini P, Thenmozhi D (2019) Grape leaf disease identification using machine learning techniques. in 2019 International Conference on Computational Intelligence in Data Science (ICCIDS). 2019. IEEE","key":"16602_CR21","DOI":"10.1109\/ICCIDS.2019.8862084"},{"issue":"8","key":"16602_CR22","doi-asserted-by":"publisher","first-page":"79","DOI":"10.21833\/ijaas.2017.08.012","volume":"4","author":"P Kaur","year":"2017","unstructured":"Kaur P, Singla S, Singh S (2017) Detection and classification of leaf diseases using integrated approach of support vector machine and particle swarm optimization. Int J Adv Appl Sci 4(8):79\u201383","journal-title":"Int J Adv Appl Sci"},{"issue":"2","key":"16602_CR23","first-page":"0975","volume":"18","author":"GPR Kranth","year":"2018","unstructured":"Kranth GPR et al (2018) Plant disease prediction using machine learning algorithms. Int J Comput Appl 18(2):0975\u20138887","journal-title":"Int J Comput Appl"},{"issue":"1","key":"16602_CR24","doi-asserted-by":"publisher","first-page":"11","DOI":"10.3390\/sym10010011","volume":"10","author":"B Liu","year":"2017","unstructured":"Liu B et al (2017) Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry 10(1):11","journal-title":"Symmetry"},{"issue":"12","key":"16602_CR25","doi-asserted-by":"publisher","first-page":"3535","DOI":"10.3390\/s20123535","volume":"20","author":"Q Yan","year":"2020","unstructured":"Yan Q et al (2020) Apple leaf diseases recognition based on an improved convolutional neural network. Sensors 20(12):3535","journal-title":"Sensors"},{"key":"16602_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105712","volume":"177","author":"Y Xiong","year":"2020","unstructured":"Xiong Y et al (2020) Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded dataset. Comput Electron Agric 177:105712","journal-title":"Comput Electron Agric"},{"unstructured":"Simonyan K and Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","key":"16602_CR27"},{"doi-asserted-by":"crossref","unstructured":"He K et al (2016) Deep residual learning for image recognition. in Proceedings of the IEEE conference on computer vision and pattern recognition","key":"16602_CR28","DOI":"10.1109\/CVPR.2016.90"},{"unstructured":"Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. in International conference on machine learning. PMLR","key":"16602_CR29"},{"key":"16602_CR30","doi-asserted-by":"publisher","first-page":"104852","DOI":"10.1016\/j.compag.2019.104852","volume":"163","author":"G Hu","year":"2019","unstructured":"Hu G et al (2019) A low shot learning method for tea leaf\u2019s disease identification. Comput Electron Agric 163:104852. https:\/\/doi.org\/10.1016\/j.compag.2019.104852","journal-title":"Comput Electron Agric"},{"doi-asserted-by":"crossref","unstructured":"Pan SJ, Yang Q (2009) A survey on transfer learning.\u00a0IEEE Trans Knowl Data Eng\u00a022(10):1345\u20131359","key":"16602_CR31","DOI":"10.1109\/TKDE.2009.191"},{"doi-asserted-by":"crossref","unstructured":"Fang S, Yuan Y et al (2017) Crop disease image recognition based on transfer learning. In Image and Graphics: 9th International Conference, Part I 9:545\u2013554","key":"16602_CR32","DOI":"10.1007\/978-3-319-71607-7_48"},{"doi-asserted-by":"crossref","unstructured":"Deng J et al (2009) ImageNet: A large-scale hierarchical image database. in 2009 IEEE Conference on Computer Vision and Pattern Recognition","key":"16602_CR33","DOI":"10.1109\/CVPR.2009.5206848"},{"unstructured":"Lin M, Chen Q, Yan S (2013) Network in network. arXiv preprint arXiv:1312.4400","key":"16602_CR34"},{"doi-asserted-by":"publisher","unstructured":"Chao X et al (2020) Identification of Apple Tree Leaf Diseases Based on Deep Learning Models. Symmetry. 12(7).https:\/\/doi.org\/10.3390\/sym12071065","key":"16602_CR35","DOI":"10.3390\/sym12071065"},{"doi-asserted-by":"crossref","unstructured":"Tang M, Gorelick L et al (2013) Grabcut in one cut. In Proceedings of the IEEE international conference on computer vision:1769\u20131776","key":"16602_CR36","DOI":"10.1109\/ICCV.2013.222"},{"doi-asserted-by":"crossref","unstructured":"Cheng MM et al (2015). Densecut: Densely connected crfs for realtime grabcut. In Comput Graphics Forum 34(7):193\u2013201","key":"16602_CR37","DOI":"10.1111\/cgf.12758"},{"issue":"12","key":"16602_CR38","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1049\/iet-ipr.2016.0009","volume":"10","author":"R Fu","year":"2016","unstructured":"Fu R et al (2016) Fully automatic figure-ground segmentation algorithm based on deep convolutional neural network and GrabCut. IET Image Proc 10(12):937\u2013942","journal-title":"IET Image Proc"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16602-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16602-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16602-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T10:52:36Z","timestamp":1709203956000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16602-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,1]]},"references-count":38,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["16602"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16602-4","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2023,9,1]]},"assertion":[{"value":"29 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2023","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.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}}]}}