{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T05:25:32Z","timestamp":1776489932078,"version":"3.51.2"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"24","license":[{"start":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T00:00:00Z","timestamp":1705363200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T00:00:00Z","timestamp":1705363200000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-18099-3","type":"journal-article","created":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T05:02:24Z","timestamp":1705381344000},"page":"64533-64549","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Detection of plant leaf diseases using deep convolutional neural network models"],"prefix":"10.1007","volume":"83","author":[{"given":"Puja","family":"Singla","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1079-2449","authenticated-orcid":false,"given":"Vijaya","family":"Kalavakonda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7570-8351","authenticated-orcid":false,"given":"Ramalingam","family":"Senthil","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,16]]},"reference":[{"key":"18099_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105220","volume":"170","author":"SH Lee","year":"2020","unstructured":"Lee SH, Go\u00ebau H, Bonnet P, Joly A (2020) New perspectives on plant disease characterization based on deep learning. Comput Electron Agric 170:105220. https:\/\/doi.org\/10.1016\/j.compag.2020.105220","journal-title":"Comput Electron Agric"},{"key":"18099_CR2","doi-asserted-by":"publisher","unstructured":"Fisher MC, Gurr SJ, Cuomo CA, Blehert DS, Jin H et al (2020) Threats posed by the fungal kingdom to humans, wildlife, and agriculture. MBio 11(3). https:\/\/doi.org\/10.1128\/mBio.00449-20","DOI":"10.1128\/mBio.00449-20"},{"key":"18099_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106523","volume":"191","author":"P Gui","year":"2021","unstructured":"Gui P, Dang W, Zhu F, Zhao Q (2021) Towards automatic field plant disease recognition. Comput Electron Agric 191:106523. https:\/\/doi.org\/10.1016\/j.compag.2021.106523","journal-title":"Comput Electron Agric"},{"key":"18099_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107054","volume":"198","author":"M Astani","year":"2022","unstructured":"Astani M, Hasheminejad M, Vaghefi M (2022) A diverse ensemble classifier for tomato disease recognition. Comput Electron Agric 198:107054. https:\/\/doi.org\/10.1016\/j.compag.2022.107054","journal-title":"Comput Electron Agric"},{"key":"18099_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.106892","volume":"196","author":"X Fan","year":"2022","unstructured":"Fan X, Luo P, Mu Y, Zhou R, Tjahjadi T, Ren Y (2022) Leaf image based plant disease identification using transfer learning and feature fusion. Comput Electron Agric 196:106892. https:\/\/doi.org\/10.1016\/j.compag.2022.106892","journal-title":"Comput Electron Agric"},{"key":"18099_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105393","volume":"173","author":"J Chen","year":"2020","unstructured":"Chen J, Chen J, Zhang D, Sun Y, Nanehkaran YA (2020) Using deep transfer learning for image-based plant disease identification. Comput Electron Agric 173:105393. https:\/\/doi.org\/10.1016\/j.compag.2020.105393","journal-title":"Comput Electron Agric"},{"key":"18099_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105803","volume":"178","author":"Y Li","year":"2020","unstructured":"Li Y, Nie J, Chao X (2020) Do we really need deep CNN for plant diseases identification? Comput Electron Agric 178:105803. https:\/\/doi.org\/10.1016\/j.compag.2020.105803","journal-title":"Comput Electron Agric"},{"key":"18099_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105933","volume":"86","author":"R Karthik","year":"2020","unstructured":"Karthik R, Hariharan M, Anand S, Mathikshara P, Johnson A, Menaka R (2020) Attention embedded residual CNN for disease detection in tomato leaves. Appl Soft Comput 86:105933. https:\/\/doi.org\/10.1016\/j.asoc.2019.105933","journal-title":"Appl Soft Comput"},{"key":"18099_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2020.101182","volume":"61","author":"\u00dc Atila","year":"2021","unstructured":"Atila \u00dc, U\u00e7ar M, Akyol K, U\u00e7ar E (2021) Plant leaf disease classification using EfficientNet deep learning model. Ecol Inform 61:101182. https:\/\/doi.org\/10.1016\/j.ecoinf.2020.101182","journal-title":"Ecol Inform"},{"key":"18099_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jafr.2023.100675","volume":"14","author":"MV Shewale","year":"2023","unstructured":"Shewale MV, Daruwala RD (2023) High performance deep learning architecture for early detection and classification of plant leaf disease. J Agric Food Res 14:100675. https:\/\/doi.org\/10.1016\/j.jafr.2023.100675","journal-title":"J Agric Food Res"},{"key":"18099_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105527","volume":"175","author":"PK Sethy","year":"2020","unstructured":"Sethy PK, Barpanda NK, Rath AK, Behera SK (2020) Deep feature based rice leaf disease identification using support vector machine. Comput Electron Agric 175:105527. https:\/\/doi.org\/10.1016\/j.compag.2020.105527","journal-title":"Comput Electron Agric"},{"key":"18099_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106279","volume":"187","author":"A Abbas","year":"2021","unstructured":"Abbas A, Jain S, Gour M, Vankudothu S (2021) Tomato plant disease detection using transfer learning with C-GAN synthetic images. Comput Electron Agric 187:106279. https:\/\/doi.org\/10.1016\/j.compag.2021.106279","journal-title":"Comput Electron Agric"},{"key":"18099_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106184","volume":"186","author":"Z Jiang","year":"2021","unstructured":"Jiang Z, Dong Z, Jiang W, Yang Y (2021) Recognition of rice leaf diseases and wheat leaf diseases based on multi-task deep transfer learning. Comput Electron Agric 186:106184. https:\/\/doi.org\/10.1016\/j.compag.2021.106184","journal-title":"Comput Electron Agric"},{"key":"18099_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106658","volume":"193","author":"S Dananjayan","year":"2022","unstructured":"Dananjayan S, Tang Y, Zhuang J, Hou C, Luo S (2022) Assessment of state-of-the-art deep learning based citrus disease detection techniques using annotated optical leaf images. Comput Electron Agric 193:106658. https:\/\/doi.org\/10.1016\/j.compag.2021.106658","journal-title":"Comput Electron Agric"},{"key":"18099_CR15","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1016\/j.compag.2020.105341","journal-title":"Comput Electron Agric"},{"key":"18099_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.106718","volume":"193","author":"M Ji","year":"2022","unstructured":"Ji M, Wu Z (2022) Automatic detection and severity analysis of grape black measles disease based on deep learning and fuzzy logic. Comput Electron Agric 193:106718. https:\/\/doi.org\/10.1016\/j.compag.2022.106718","journal-title":"Comput Electron Agric"},{"key":"18099_CR17","doi-asserted-by":"publisher","unstructured":"Ravi V, Acharya V, Pham TD (2022) Attention deep learning-based large-scale learning classifier for cassava leaf disease classification. Expert Syst 39(2). https:\/\/doi.org\/10.1111\/exsy.12862","DOI":"10.1111\/exsy.12862"},{"key":"18099_CR18","doi-asserted-by":"publisher","first-page":"19217","DOI":"10.1007\/s00521-022-07521-w","volume":"34","author":"NS Russel","year":"2022","unstructured":"Russel NS, Selvaraj A (2022) Leaf species and disease classification using multiscale parallel deep CNN architecture. Neural Comput Appl 34:19217\u201319237. https:\/\/doi.org\/10.1007\/s00521-022-07521-w","journal-title":"Neural Comput Appl"},{"key":"18099_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107206","volume":"200","author":"D Xiao","year":"2022","unstructured":"Xiao D, Zeng R, Liu Y, Huang Y, Liu J, Feng J, Zhang X (2022) Citrus greening disease recognition algorithm based on classification network using TRL-GAN. Comput Electron Agric 200:107206. https:\/\/doi.org\/10.1016\/j.compag.2022.107206","journal-title":"Comput Electron Agric"},{"key":"18099_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106644","volume":"193","author":"Y Zhao","year":"2022","unstructured":"Zhao Y, Sun C, Xu X, Chen J (2022) RIC-net: A plant disease classification model based on the fusion of inception and residual structure and embedded attention mechanism. Comput Electron Agric 193:106644. https:\/\/doi.org\/10.1016\/j.compag.2021.106644","journal-title":"Comput Electron Agric"},{"issue":"7","key":"18099_CR21","doi-asserted-by":"publisher","first-page":"4993","DOI":"10.1007\/s10462-020-09813-w","volume":"53","author":"R Cristin","year":"2020","unstructured":"Cristin R, Santhosh Kumar B, Priya C, Karthick K (2020) Deep neural network based Rider-Cuckoo Search Algorithm for plant disease detection. Artif intel Rev 53(7):4993\u20135018. https:\/\/doi.org\/10.1007\/s10462-020-09813-w","journal-title":"Artif intel Rev"},{"key":"18099_CR22","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.3389\/fpls.2016.01419","volume":"7","author":"P MohantySharada","year":"2016","unstructured":"MohantySharada P, Hughes DP, Salath\u00e9 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"},{"issue":"10","key":"18099_CR23","doi-asserted-by":"publisher","first-page":"2441","DOI":"10.3390\/foods10102441","volume":"10","author":"L Chen","year":"2021","unstructured":"Chen L, Cui X, Li W (2021) Meta-learning for few-shot plant disease detection. Foods 10(10):2441. https:\/\/doi.org\/10.3390\/foods10102441","journal-title":"Foods"},{"issue":"12","key":"18099_CR24","doi-asserted-by":"publisher","first-page":"1388","DOI":"10.3390\/electronics10121388","volume":"10","author":"SM Hassan","year":"2021","unstructured":"Hassan SM, Maji AK, Masi\u0144ski M, Leonowicz Z, Jasi\u0144ska E (2021) Identification of plant-leaf diseases using CNN and transfer-learning approach. Electronics 10(12):1388. https:\/\/doi.org\/10.3390\/electronics10121388","journal-title":"Electronics"},{"key":"18099_CR25","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.aiia.2020.10.002","volume":"4","author":"V Singh","year":"2021","unstructured":"Singh V, Sharma N, Singh S (2021) A review of imaging techniques for plant disease detection. Artif Intell Agric 4:229\u2013242. https:\/\/doi.org\/10.1016\/j.aiia.2020.10.002","journal-title":"Artif Intell Agric"},{"issue":"19","key":"18099_CR26","doi-asserted-by":"publisher","first-page":"3188","DOI":"10.3390\/rs12193188","volume":"12","author":"N Zhang","year":"2020","unstructured":"Zhang N, Yang G, Pan Y, Yang X, Chen L, Zhao C (2020) A review of advanced technologies and development for hyperspectral-based plant disease detection in the past three decades. Remote Sens 12(19):3188. https:\/\/doi.org\/10.3390\/rs12193188","journal-title":"Remote Sens"},{"key":"18099_CR27","doi-asserted-by":"publisher","first-page":"575","DOI":"10.3390\/s22020575","volume":"22","author":"P Kaur","year":"2022","unstructured":"Kaur P, Harnal S, Tiwari R, Upadhyay S, Bhatia S, Mashat A, Alabdali AM (2022) Recognition of leaf disease using hybrid convolutional neural network by applying feature reduction. Sensors 22:575. https:\/\/doi.org\/10.3390\/s22020575","journal-title":"Sensors"},{"key":"18099_CR28","doi-asserted-by":"publisher","first-page":"6051","DOI":"10.1007\/s11042-021-11763-6","volume":"81","author":"RK Singh","year":"2022","unstructured":"Singh RK, Tiwari A, Gupta RK (2022) Deep transfer modeling for classification of Maize Plant Leaf Disease. Multimed Tools Appl 81:6051\u20136067. https:\/\/doi.org\/10.1007\/s11042-021-11763-6","journal-title":"Multimed Tools Appl"},{"key":"18099_CR29","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1186\/s40537-020-00332-7","volume":"7","author":"HF Pardede","year":"2020","unstructured":"Pardede HF, Suryawati E, Zilvan V, Ramdan A, Kusumo RBS, Heryana A, Yuwana RS, Krisnandi D, Subekti A, Fauziah F, Rahadi VP (2020) Plant diseases detection with low resolution data using nested skip connections. J Big Data 7:57. https:\/\/doi.org\/10.1186\/s40537-020-00332-7","journal-title":"J Big Data"},{"key":"18099_CR30","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.aiia.2021.05.002","volume":"5","author":"P Bedi","year":"2021","unstructured":"Bedi P, Gole P (2021) Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network. Artif Intell Agric 5:90\u2013101. https:\/\/doi.org\/10.1016\/j.aiia.2021.05.002","journal-title":"Artif Intell Agric"},{"key":"18099_CR31","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/s40747-021-00536-1","volume":"8","author":"W Albattah","year":"2022","unstructured":"Albattah W, Nawaz M, Javed A, Masood M, Albahli S (2022) A novel deep learning method for detection and classification of plant diseases. Complex Intell Syst 8:507\u2013524. https:\/\/doi.org\/10.1007\/s40747-021-00536-1","journal-title":"Complex Intell Syst"},{"key":"18099_CR32","doi-asserted-by":"publisher","unstructured":"Sharma S, Sharma G, Menghani E, Sharma A (2023) A comprehensive review on automatic detection and early prediction of tomato diseases and pests control based on leaf\/fruit images, Lect Notes Netw Sys 599 LNNS, pp 276\u2013296. https:\/\/doi.org\/10.1007\/978-3-031-22018-0_26","DOI":"10.1007\/978-3-031-22018-0_26"},{"key":"18099_CR33","doi-asserted-by":"publisher","unstructured":"Karthika I, Megha M, Roshni M (2023) deep learning approach to automated tomato plant leaf disease diagnosis. Proceedings of the 2023 2nd International Conference on Electronics and Renewable Systems, ICEARS 2023, pp 1381\u20131388. https:\/\/doi.org\/10.1109\/ICEARS56392.2023.10085564","DOI":"10.1109\/ICEARS56392.2023.10085564"},{"key":"18099_CR34","doi-asserted-by":"publisher","unstructured":"Kukadiya H, Meva D (2022) Automatic cotton leaf disease classification and detection by convolutional neural network. Communications in Computer and Information Science, 1759 CCIS, pp 247\u2013266. https:\/\/doi.org\/10.1007\/978-3-031-23092-9_20","DOI":"10.1007\/978-3-031-23092-9_20"},{"key":"18099_CR35","doi-asserted-by":"publisher","unstructured":"Shukla PK, Sathiya S (2022) Early detection of potato leaf diseases using convolutional neural network with web application. Proceedings - 2022 IEEE World Conference on Applied Intelligence and Computing, AIC 2022, pp 277\u2013282. https:\/\/doi.org\/10.1109\/AIC55036.2022.9848975","DOI":"10.1109\/AIC55036.2022.9848975"},{"key":"18099_CR36","doi-asserted-by":"publisher","unstructured":"Paiva-Peredo E (2023) Deep learning for the classification of cassava leaf diseases in unbalanced field data set. Communications in Computer and Information Science, 1798 CCIS, pp 101\u2013114. https:\/\/doi.org\/10.1007\/978-3-031-28183-9_8","DOI":"10.1007\/978-3-031-28183-9_8"},{"key":"18099_CR37","doi-asserted-by":"publisher","unstructured":"Yadav R, Pandey M, Sahu SK (2022) Cassava plant disease detection with imbalanced dataset using transfer learning. Proceedings - 2022 IEEE World Conference on Applied Intelligence and Computing, AIC 2022, pp 220\u2013225. https:\/\/doi.org\/10.1109\/AIC55036.2022.9848882","DOI":"10.1109\/AIC55036.2022.9848882"},{"key":"18099_CR38","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.compeleceng.2019.04.011","volume":"76","author":"G Geetharamani","year":"2019","unstructured":"Geetharamani G, Arun Pandian J (2019) Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Comput Electr Eng 76:323\u2013338. https:\/\/doi.org\/10.1016\/j.compeleceng.2019.04.011","journal-title":"Comput Electr Eng"},{"issue":"2","key":"18099_CR39","first-page":"279","volume":"99","author":"D Rosmala","year":"2021","unstructured":"Rosmala D, PrakhaAnggara MR, Sahat JP (2021) Transfer learning with VGG16 and InceptionV3 model for classification of potato leaf disease. J Theor Appl Inf Technol 99(2):279\u2013292","journal-title":"J Theor Appl Inf Technol"},{"key":"18099_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106191","volume":"186","author":"LM Tassis","year":"2021","unstructured":"Tassis LM, Tozzi de Souza JE, Krohling RA (2021) A deep learning approach combining instance and semantic segmentation to identify diseases and pests of coffee leaves from in-field images. Comput Electron Agric 186:106191. https:\/\/doi.org\/10.1016\/j.compag.2021.106191","journal-title":"Comput Electron Agric"},{"key":"18099_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2021.101289","volume":"63","author":"V Tiwari","year":"2021","unstructured":"Tiwari V, Joshi RC, Dutta MK (2021) Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images. Ecol Inform 63:101289. https:\/\/doi.org\/10.1016\/j.ecoinf.2021.101289","journal-title":"Ecol Inform"},{"issue":"8","key":"18099_CR42","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.13041","volume":"39","author":"V Tiwari","year":"2022","unstructured":"Tiwari V, Joshi RC, Dutta MK (2022) Deep neural network for multi-class classification of medicinal plant leaves. Expert Syst 39(8):e13041. https:\/\/doi.org\/10.1111\/exsy.13041","journal-title":"Expert Syst"},{"issue":"3","key":"18099_CR43","doi-asserted-by":"publisher","first-page":"284","DOI":"10.3844\/JCSSP.2021.284.295","volume":"17","author":"A Ennouni","year":"2021","unstructured":"Ennouni A, Sihamman NO, Sabri MA, Aarab A (2021) Early detection and classification approach for plant diseases based on MultiScale image decomposition. J Comput Sci 17(3):284\u2013295. https:\/\/doi.org\/10.3844\/JCSSP.2021.284.295","journal-title":"J Comput Sci"},{"key":"18099_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105661","volume":"177","author":"U Barman","year":"2020","unstructured":"Barman U, Choudhury RD, Sahu D, Barman GG (2020) Comparison of convolution neural networks for smartphone image based real time classification of citrus leaf disease. Comput Electron Agric 177:105661. https:\/\/doi.org\/10.1016\/j.compag.2020.105661","journal-title":"Comput Electron Agric"},{"key":"18099_CR45","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.biosystemseng.2020.03.020","volume":"194","author":"CR Rahman","year":"2020","unstructured":"Rahman CR, Arko PS, Ali ME, Iqbal Khan MA, Apon SH, Nowrin F, Wasif A (2020) Identification and recognition of rice diseases and pests using convolutional neural networks. Biosyst Eng 194:112\u2013120. https:\/\/doi.org\/10.1016\/j.biosystemseng.2020.03.020","journal-title":"Biosyst Eng"},{"issue":"3","key":"18099_CR46","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1007\/s41348-022-00601-y","volume":"129","author":"BT Hanh","year":"2022","unstructured":"Hanh BT, Van Manh H, Nguyen N (2022) Enhancing the performance of transferred efficientnet models in leaf image-based plant disease classification. J Plant Dis Prot 129(3):623\u2013634. https:\/\/doi.org\/10.1007\/s41348-022-00601-y","journal-title":"J Plant Dis Prot"},{"issue":"11","key":"18099_CR47","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.18520\/cs\/v122\/i11\/1315-1320","volume":"122","author":"Y Kumar","year":"2022","unstructured":"Kumar Y, Hasteer N, Bhardwaj A, Yogesh (2022) Convolutional neural network architecture for detection and classification of diseases in fruits. Curr Sci 122(11):1315\u20131320. https:\/\/doi.org\/10.18520\/cs\/v122\/i11\/1315-1320","journal-title":"Curr Sci"},{"issue":"8","key":"18099_CR48","doi-asserted-by":"publisher","first-page":"4967","DOI":"10.1002\/int.22747","volume":"37","author":"FG Waldamichael","year":"2022","unstructured":"Waldamichael FG, Debelee TG, Ayano YM (2022) Coffee disease detection using a robust HSV color-based segmentation and transfer learning for use on smartphones. Int J Intell Syst 37(8):4967\u20134993. https:\/\/doi.org\/10.1002\/int.22747","journal-title":"Int J Intell Syst"},{"issue":"Suppl 2","key":"18099_CR49","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/s10516-021-09610-2","volume":"32","author":"V Matarese","year":"2022","unstructured":"Matarese V (2022) Kinds of replicability: different terms and different functions. Axiomathes 32(Suppl 2):647\u2013670. https:\/\/doi.org\/10.1007\/s10516-021-09610-2","journal-title":"Axiomathes"},{"key":"18099_CR50","doi-asserted-by":"publisher","DOI":"10.1038\/nature.2016.20504","author":"M Baker","year":"2020","unstructured":"Baker M (2020) Why scientists must share their research code. Nature. https:\/\/doi.org\/10.1038\/nature.2016.20504","journal-title":"Nature"},{"issue":"6","key":"18099_CR51","first-page":"385","volume":"5","author":"SM Idicula","year":"2007","unstructured":"Idicula SM, David Peter S (2007) A multilingual query processing system using software agents. J Digit Inf Manag 5(6):385\u2013390","journal-title":"J Digit Inf Manag"},{"issue":"5","key":"18099_CR52","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1017\/S1351324918000141","volume":"24","author":"C Derici","year":"2018","unstructured":"Derici C, Aydin Y, Yenialaca C, Aydin NY, Kartal G, \u00d6zg\u00fcr A, G\u00fcng\u00f6r T (2018) A closed-domain question answering framework using reliable resources to assist students. Nat Lang Eng 24(5):725\u2013762. https:\/\/doi.org\/10.1017\/S1351324918000141","journal-title":"Nat Lang Eng"},{"key":"18099_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.atech.2023.100301","volume":"5","author":"MI Hossain","year":"2023","unstructured":"Hossain MI, Jahan S, Al Asif MR, Samsuddoha M, Ahmed K (2023) Detecting tomato leaf diseases by image processing through deep convolutional neural networks. Smart Agricultural Technology 5:100301. https:\/\/doi.org\/10.1016\/j.atech.2023.100301","journal-title":"Smart Agricultural Technology"},{"key":"18099_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.bcab.2023.102726","volume":"50","author":"G Singh","year":"2023","unstructured":"Singh G, Yogi KK (2023) Comparison of RSNET model with existing models for potato leaf disease detection. Biocatal Agric Biotechnol 50:102726. https:\/\/doi.org\/10.1016\/j.bcab.2023.102726","journal-title":"Biocatal Agric Biotechnol"},{"issue":"20","key":"18099_CR55","doi-asserted-by":"publisher","first-page":"14855","DOI":"10.1007\/s00521-023-08496-y","volume":"35","author":"P Hari","year":"2023","unstructured":"Hari P, Singh MP (2023) A lightweight convolutional neural network for disease detection of fruit leaves. Neural Comput Appl 35(20):14855\u201314866. https:\/\/doi.org\/10.1007\/s00521-023-08496-y","journal-title":"Neural Comput Appl"},{"issue":"2","key":"18099_CR56","doi-asserted-by":"publisher","first-page":"925","DOI":"10.11591\/ijeecs.v31.i2.pp925-932","volume":"31","author":"EA Mohammed","year":"2023","unstructured":"Mohammed EA, Mohammed GH (2023) Citrus leaves disease diagnosis. Indones J Electr Eng Comput Sci 31(2):925\u2013932. https:\/\/doi.org\/10.11591\/ijeecs.v31.i2.pp925-932","journal-title":"Indones J Electr Eng Comput Sci"},{"key":"18099_CR57","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.aiia.2023.07.001","volume":"9","author":"MT Ahad","year":"2023","unstructured":"Ahad MT, Li Y, Song B, Bhuiyan T (2023) Comparison of CNN-based deep learning architectures for rice diseases classification. Artif Intell Agric 9:22\u201335. https:\/\/doi.org\/10.1016\/j.aiia.2023.07.001","journal-title":"Artif Intell Agric"},{"key":"18099_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.jafr.2023.100764","volume":"14","author":"MM Islam","year":"2023","unstructured":"Islam MM, Adil MAA, Talukder MA, Ahamed MKU, Uddin MA, Hasan MK, Sharmin S, Rahman MM, Debnath SK (2023) DeepCrop: deep learning-based crop disease prediction with web application. J Agric Food Res 14:100764. https:\/\/doi.org\/10.1016\/j.jafr.2023.100764","journal-title":"J Agric Food Res"},{"issue":"24","key":"18099_CR59","doi-asserted-by":"publisher","first-page":"37151","DOI":"10.1007\/s11042-023-14954-5","volume":"82","author":"P Singh","year":"2023","unstructured":"Singh P, Singh P, Farooq U, Khurana SS, Verma JK, Kumar M (2023) CottonLeafNet: cotton plant leaf disease detection using deep neural networks. Multimed Tools Appl 82(24):37151\u201337176. https:\/\/doi.org\/10.1007\/s11042-023-14954-5","journal-title":"Multimed Tools Appl"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-18099-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-18099-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-18099-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T17:44:54Z","timestamp":1720460694000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-18099-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,16]]},"references-count":59,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["18099"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-18099-3","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,16]]},"assertion":[{"value":"11 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 October 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2024","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 related data are discussed in the manuscript and the relevant resources are provided as follows.Data: Codes: Binary Classification: Multi Classification\/Web Application:","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Resources"}},{"value":"Trial registration is not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Trial registration"}},{"value":"The authors declare no potential conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}