{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T16:03:55Z","timestamp":1771517035815,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T00:00:00Z","timestamp":1625011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T00:00:00Z","timestamp":1625011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2021,10]]},"DOI":"10.1007\/s11063-021-10521-x","type":"journal-article","created":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T17:03:06Z","timestamp":1625072586000},"page":"3653-3676","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Progressive Transfer Learning Approach for Identifying the Leaf Type by Optimizing Network Parameters"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2127-6279","authenticated-orcid":false,"given":"Deepa","family":"Joshi","sequence":"first","affiliation":[]},{"given":"Vidyanand","family":"Mishra","sequence":"additional","affiliation":[]},{"given":"Honey","family":"Srivastav","sequence":"additional","affiliation":[]},{"given":"Diksha","family":"Goel","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,30]]},"reference":[{"key":"10521_CR1","doi-asserted-by":"publisher","unstructured":"Pandey MM, Rastogi S, Rawat AKS (2013) Indian traditional ayurvedic system of medicine and nutritional supplementation. Evid Based Complementary Altern Med 2013:376327. https:\/\/doi.org\/10.1155\/2013\/376327","DOI":"10.1155\/2013\/376327"},{"key":"10521_CR2","doi-asserted-by":"crossref","unstructured":"Dileep MR, Pournami PN (2019) Ayurleaf: a deep learning approach for classification of medicinal plants","DOI":"10.1109\/TENCON.2019.8929394"},{"key":"10521_CR3","unstructured":"Smith LN (2018) A disciplined approach to neural network hyper-parameters Part 1 \u2013 learning rate, batch size, momentum, and weight decay. March, pp 464\u201372, http:\/\/arxiv.org\/abs\/1803.09820 [cs, stat]"},{"issue":"6","key":"10521_CR4","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1109\/LSP.2018.2809688","volume":"25","author":"J Hu","year":"2018","unstructured":"Hu J, Chen Z, Yang M, Zhang R, Cui Y (2018) A multiscale fusion convolutional neural network for plant leaf recognition. IEEE Signal Process Lett 25(6):853\u2013857","journal-title":"IEEE Signal Process Lett"},{"key":"10521_CR5","doi-asserted-by":"crossref","unstructured":"Wu SG, Bao FS, Xu EY, Wang YX, Chang YF, Xiang QL (2007) A Leaf Recognition Algorithm for Plant Classification using Probabilistic Neural Network. In: 7th IEEE international symposium on signal processing and information technology, Giza, Egypt, pp 11\u201316","DOI":"10.1109\/ISSPIT.2007.4458016"},{"key":"10521_CR6","doi-asserted-by":"crossref","unstructured":"Hossain J, Amin MA (2010) Leaf Shape Identification Based Plant Biometrics. In: 13th international conference on computer and information technology, Dhaka, Bangladesh, pp 458\u2013463","DOI":"10.1109\/ICCITECHN.2010.5723901"},{"key":"10521_CR7","doi-asserted-by":"publisher","first-page":"105341","DOI":"10.1016\/j.compag.2020.105341","volume":"172","author":"W Zeng","year":"2020","unstructured":"Zeng W, Li M (2020) Crop leaf disease recognition based on Self-Attention convolutional neural network. Comput Electron Agric 172:105341","journal-title":"Comput Electron Agric"},{"key":"10521_CR8","doi-asserted-by":"crossref","unstructured":"Le TL, Tran DT, Hoang VN (2014) Fully Automatic leaf-based plant identification, application for Vietnamese medicinal plant search. In: Fifth symposium on information and communication technology, Hanoi, Vietnam, pp 146\u2013154","DOI":"10.1145\/2676585.2676592"},{"key":"10521_CR9","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.neucom.2012.03.028","volume":"116","author":"JX Du","year":"2013","unstructured":"Du JX, Zhai CM, Wang QP (2013) Recognition of plant leaf image based on fractal dimension features. Neurocomputing 116:150\u2013156","journal-title":"Neurocomputing"},{"key":"10521_CR10","doi-asserted-by":"crossref","unstructured":"Lee SH, Chan CS, Wilkin P, Remagnino P (2015) Deep-plant: Plant identification with convolutional neural networks. In: Image Processing (ICIP), 2015 IEEE International Conference on 2015 Sep 27, pp 452\u2013456","DOI":"10.1109\/ICIP.2015.7350839"},{"key":"10521_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patcog.2017.05.015","volume":"71","author":"SH Lee","year":"2017","unstructured":"Lee SH, Chan CS, Mayo SJ, Remagnino P (2017) How deep learning extracts and learns leaf features for plant classification. Pattern Recogn 71:1\u201313","journal-title":"Pattern Recogn"},{"key":"10521_CR12","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.biosystemseng.2015.08.003","volume":"139","author":"A Aakif","year":"2015","unstructured":"Aakif A, Khan MF (2015) Automatic classification of plants based on their leaves. Biosyst Eng 139:66\u201375","journal-title":"Biosyst Eng"},{"key":"10521_CR13","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/j.amc.2006.07.072","volume":"185","author":"JX Du","year":"2007","unstructured":"Du JX, Wang XF, Zhang GJ (2007) Leaf shape based plant species recognition. Appl Math Comput 185:883\u2013893","journal-title":"Appl Math Comput"},{"key":"10521_CR14","unstructured":"Herdiyeni Y, Wahyuni NKS (2012) Mobile Application for Indonesian Medicinal Plants Identification using Fuzzy Local Binary Pattern and Fuzzy Color Histogram. In: international conference on advanced computer science and information systems (ICACSIS), West Java, Indonesia, pp 301\u2013306"},{"key":"10521_CR15","doi-asserted-by":"publisher","first-page":"e563","DOI":"10.7717\/peerj.563","volume":"2","author":"A Hernandez-Serna","year":"2014","unstructured":"Hernandez-Serna A, Jim\u00e9nez-Segura LF (2014) Automatic Identification of species with neural networks. PeerJ 2:e563. https:\/\/doi.org\/10.7717\/peerj.563","journal-title":"PeerJ"},{"key":"10521_CR16","doi-asserted-by":"crossref","unstructured":"Siravenha ACQ, Carvalho SR (2015) Exploring the use of Leaf Shape Frequencies for Plant Classification. In: 28th SIBGRAPI conference on graphics, patterns and images, Salvador, Brazil, pp 297\u2013304","DOI":"10.1109\/SIBGRAPI.2015.36"},{"key":"10521_CR17","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.patrec.2015.02.010","volume":"58","author":"J Chaki","year":"2015","unstructured":"Chaki J, Parekh R, Bhattacharya S (2015) Plant leaf recognition using texture and shape features with neural classifiers. Pattern Recogn Lett 58:61\u201368","journal-title":"Pattern Recogn Lett"},{"key":"10521_CR18","doi-asserted-by":"crossref","unstructured":"Tan JW, Chang S, Abdul Kareem SB, Yap HJ, Yong K (2018) Deep learning for plant species classification using leaf vein morphometric. IEEE\/ACM Trans Comput Biol Bioinform 17(1):82\u201390","DOI":"10.1109\/TCBB.2018.2848653"},{"key":"10521_CR19","doi-asserted-by":"crossref","unstructured":"Janani R, Gopal A (2013) Identification of selected medicinal plant leaves using image features and ANN. In: 2013 international conference on advanced electronic systems (ICAES), September, pp 238\u2013242","DOI":"10.1109\/ICAES.2013.6659400"},{"key":"10521_CR20","doi-asserted-by":"crossref","unstructured":"Venkataraman D, Mangayarkarasi N (2016) Computer vision-based feature extraction of leaves for identification of medicinal values of plants. In: 2016 IEEE international conference on computational intelligence and computing research (ICCIC), December, pp 1\u20135","DOI":"10.1109\/ICCIC.2016.7919637"},{"key":"10521_CR21","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1016\/j.compag.2016.07.003","volume":"127","author":"GL Grinblat","year":"2016","unstructured":"Grinblat GL, Uzal LC, Larese MG, Granitto PM (2016) Deep learning for plant identification using vein morphological patterns. Comput Electron Agric 127:418\u2013424","journal-title":"Comput Electron Agric"},{"key":"10521_CR22","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"},{"issue":"1","key":"10521_CR23","doi-asserted-by":"publisher","first-page":"6","DOI":"10.3390\/computers9010006","volume":"9","author":"S Sabzi","year":"2020","unstructured":"Sabzi S, Pourdarbani R, Arribas JI (2020) A computer vision system for the automatic classification of five varieties of tree leaf images. Computers 9(1):6","journal-title":"Computers"},{"key":"10521_CR24","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.cviu.2014.11.001","volume":"133","author":"C Kalyoncu","year":"2015","unstructured":"Kalyoncu C, Toygar \u00d6 (2015) Geometric leaf classification. Comput Vis Image Underst 133:102\u2013109","journal-title":"Comput Vis Image Underst"},{"issue":"1","key":"10521_CR25","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.compag.2011.05.007","volume":"78","author":"JI Arribas","year":"2011","unstructured":"Arribas JI, S\u00e1nchez-Ferrero GV, Ruiz-Ruiz G, G\u00f3mez-Gil J (2011) Leaf classification in sunflower crops by computer vision and neural networks. Comput Electron Agric 78(1):9\u201318","journal-title":"Comput Electron Agric"},{"key":"10521_CR26","doi-asserted-by":"crossref","unstructured":"Batvia V., Patel D., Dr. Vasant AR (2017) A Survey on Ayurvedic Medicine Classification using Tensor flow. International Journal of Computer Trends and Technology (IJCTT), Published by Seventh Sense Research Group, 2017, November, ISSN: 2231\u20132803, www.ijcttjoumal.org, V53(2): 68\u201370,","DOI":"10.14445\/22312803\/IJCTT-V53P114"},{"key":"10521_CR27","doi-asserted-by":"crossref","unstructured":"Kumar PM, Surya CM, Gopi VP (2017) Identification of ayurvedic medicinal plants by image processing of leaf samples. In: Third international conference on research in computational intelligence and communication networks (ICRCICN), November, pp 231\u2013238","DOI":"10.1109\/ICRCICN.2017.8234512"},{"key":"10521_CR28","doi-asserted-by":"crossref","unstructured":"Priya CA, Balasaravanan T, Thanamani AS (2012) An efficient leaf recognition algorithm for plant classification using support vector machine. In: Proceedings of international conference on pattern recognition, informatics and medical engineering (PRIME-2012), pp 428\u2013432","DOI":"10.1109\/ICPRIME.2012.6208384"},{"key":"10521_CR29","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1007\/978-981-13-1274-8_29","volume":"839","author":"MM Amlekar","year":"2018","unstructured":"Amlekar MM, Gaikwad AT (2018) Plant classification using image processing and neural network. Data Manag Anal Innov 839:375\u2013384","journal-title":"Data Manag Anal Innov"},{"key":"10521_CR30","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.compag.2018.12.038","volume":"157","author":"G Saleem","year":"2019","unstructured":"Saleem G, Akhtar M, Ahmed N, Qureshi WS (2019) Automated analysis of visual leaf shape features for plant classification. Comput Electron Agric 157:270\u2013280","journal-title":"Comput Electron Agric"},{"key":"10521_CR31","doi-asserted-by":"crossref","unstructured":"Shah MP, Singha S, Awate SP (2017) Leaf classification using marginalized shape context and shape+texture dual-path deep convolutional neural network. In: 2017 IEEE international conference on image processing (ICIP), September, pp 860\u2013864","DOI":"10.1109\/ICIP.2017.8296403"},{"key":"10521_CR32","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.procs.2019.05.042","volume":"152","author":"V Bodhwani","year":"2019","unstructured":"Bodhwani V, Acharjya DP, Bodhwani U (2019) Deep residual networks for plant identification. Procedia Comput Sci 152:186\u2013194","journal-title":"Procedia Comput Sci"},{"key":"10521_CR33","doi-asserted-by":"crossref","unstructured":"Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: European conference on computer vision, September, pp 818\u2013833","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"10521_CR34","unstructured":"Agarap AF (2018) Deep learning using rectified linear units (ReLU). March, arXiv preprint http:\/\/arxiv.org\/abs\/1803.08375"},{"key":"10521_CR35","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"},{"issue":"2","key":"10521_CR36","doi-asserted-by":"publisher","first-page":"125","DOI":"10.3390\/info11020125","volume":"11","author":"A Buslaev","year":"2020","unstructured":"Buslaev A, Iglovikov VI, Khvedchenya E, Parinov A, Druzhinin M, Kalinin AA (2020) Albumentations: fast and flexible image augmentations. Information 11(2):125","journal-title":"Information"},{"key":"10521_CR37","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. February, arXiv preprint http:\/\/arxiv.org\/abs\/1502.03167"},{"issue":"1","key":"10521_CR38","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"issue":"3","key":"10521_CR39","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211\u2013252","journal-title":"Int J Comput Vis"},{"issue":"10","key":"10521_CR40","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10521_CR41","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision, pp 1026\u20131034","DOI":"10.1109\/ICCV.2015.123"},{"key":"10521_CR42","unstructured":"Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, March, pp 249\u2013256"},{"key":"10521_CR43","doi-asserted-by":"crossref","unstructured":"Howard J, Ruder S (2018) Universal language model fine-tuning for text classification. January, arXiv preprint http:\/\/arxiv.org\/abs\/1801.06146","DOI":"10.18653\/v1\/P18-1031"},{"key":"10521_CR44","doi-asserted-by":"crossref","unstructured":"Smith LN (2017) Cyclical learning rates for training neural networks. In: IEEE winter conference on applications of computer vision (WACV), March, pp 464\u2013472","DOI":"10.1109\/WACV.2017.58"},{"key":"10521_CR45","unstructured":"Loshchilov I, Hutter F Sgdr (2016) Stochastic gradient descent with warm restarts, August, arXiv preprint http:\/\/arxiv.org\/abs\/1608.03983"},{"key":"10521_CR46","unstructured":"Loshchilov I, Hutter F (2017) Fixing weight decay regularization in adam. 2017, November, http:\/\/arxiv.org\/abs\/1711.05101 [cs, math]"},{"issue":"6","key":"10521_CR47","doi-asserted-by":"publisher","first-page":"4475","DOI":"10.1007\/s10462-019-09799-0","volume":"53","author":"D Joshi","year":"2020","unstructured":"Joshi D, Singh TP (2020) A survey of fracture detection techniques in bone X-ray images. Artif Intell Rev 53(6):4475\u20134517","journal-title":"Artif Intell Rev"},{"issue":"3","key":"10521_CR48","first-page":"155","volume":"14","author":"HF Eid","year":"2017","unstructured":"Eid HF, Abraham A (2017) Plant species identification using leaf biometrics and swarm optimization: a hybrid PSO, GWO, SVM model. Int J Hybrid Intell Syst 14(3):155\u2013165","journal-title":"Int J Hybrid Intell Syst"},{"key":"10521_CR49","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.ins.2014.07.028","volume":"302","author":"B Wang","year":"2015","unstructured":"Wang B, Brown D, Gao Y, La Salle J (2015) Multiscale-arch-height description for mobile retrieval of leaf images. Inf Sci 302:132\u2013148","journal-title":"Inf Sci"},{"key":"10521_CR50","first-page":"486","volume":"479","author":"P Pawara","year":"2017","unstructured":"Pawara P, Okafor E, Surinta O, Schomaker L, Wiering M (2017) comparing local descriptors and bags of visual words to deep convolutional neural networks for plant recognition. ICPRAM 479:486","journal-title":"ICPRAM"},{"key":"10521_CR51","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.ecoinf.2017.05.005","volume":"40","author":"P Barr\u00e9","year":"2017","unstructured":"Barr\u00e9 P, St\u00f6ver BC, M\u00fcller KF, Steinhage V (2017) LeafNet: a computer vision system for automatic plant species identification. Ecol Inform 40:50\u201356","journal-title":"Ecol Inform"},{"key":"10521_CR52","doi-asserted-by":"crossref","unstructured":"Kumar N, Belhumeur PN, Biswas A, Jacobs DW, Kress WJ, Lopez IC, Soares JV (2012) Leafsnap: A computer vision system for automatic plant species identification. In: European conference on computer vision, Springer, Berlin, Heidelberg, 2012, October, pp 502\u2013516.","DOI":"10.1007\/978-3-642-33709-3_36"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-021-10521-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-021-10521-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-021-10521-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,5]],"date-time":"2023-11-05T13:34:11Z","timestamp":1699191251000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-021-10521-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,30]]},"references-count":52,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,10]]}},"alternative-id":["10521"],"URL":"https:\/\/doi.org\/10.1007\/s11063-021-10521-x","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,30]]},"assertion":[{"value":"28 April 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 June 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}