{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T21:38:19Z","timestamp":1774993099061,"version":"3.50.1"},"reference-count":72,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T00:00:00Z","timestamp":1753056000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T00:00:00Z","timestamp":1753056000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Manipal University Jaipur"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Computing"],"DOI":"10.1007\/s10791-025-09679-y","type":"journal-article","created":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T09:22:02Z","timestamp":1753089722000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Fusion of non-iterative deep neural network feature extraction with kernel extreme learning machine for plant disease classification"],"prefix":"10.1007","volume":"28","author":[{"given":"Kirti","family":"Kirti","sequence":"first","affiliation":[]},{"given":"Navin","family":"Rajpal","sequence":"additional","affiliation":[]},{"given":"Virendra P.","family":"Vishwakarma","sequence":"additional","affiliation":[]},{"given":"Pramod Kumar","family":"Soni","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,21]]},"reference":[{"issue":"2","key":"9679_CR1","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1007\/s11760-021-01909-2","volume":"16","author":"M Turkoglu","year":"2022","unstructured":"Turkoglu M, Yaniko\u011flu B, Hanbay D. Plantdiseasenet: convolutional neural network ensemble for plant disease and pest detection. SIViP. 2022;16(2):301\u20139.","journal-title":"SIViP"},{"issue":"7","key":"9679_CR2","doi-asserted-by":"publisher","first-page":"1547","DOI":"10.1016\/S2095-3119(16)61497-1","volume":"16","author":"Y Qing","year":"2017","unstructured":"Qing Y, Chen G-T, Zheng W, Zhang C, Yang B-J, Jian T. Automated detection and identification of white-backed planthoppers in paddy fields using image processing. J Integr Agric. 2017;16(7):1547\u201357.","journal-title":"J Integr Agric"},{"issue":"2","key":"9679_CR3","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/s11831-019-09324-0","volume":"27","author":"SS Chouhan","year":"2020","unstructured":"Chouhan SS, Singh UP, Jain S. Applications of computer vision in plant pathology: a survey. Arch Comput Methods Eng. 2020;27(2):611\u201332.","journal-title":"Arch Comput Methods Eng"},{"key":"9679_CR4","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s41348-020-00368-0","volume":"128","author":"VK Vishnoi","year":"2021","unstructured":"Vishnoi VK, Kumar K, Kumar B. Plant disease detection using computational intelligence and image processing. J Plant Dis Prot. 2021;128:19\u201353.","journal-title":"J Plant Dis Prot"},{"key":"9679_CR5","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/s11831-016-9206-z","volume":"25","author":"J W\u00e4ldchen","year":"2018","unstructured":"W\u00e4ldchen J, M\u00e4der P. Plant species identification using computer vision techniques: a systematic literature review. Arch Comput Methods Eng. 2018;25:507\u201343.","journal-title":"Arch Comput Methods Eng"},{"key":"9679_CR6","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/s11831-018-9255-6","volume":"26","author":"S Kaur","year":"2019","unstructured":"Kaur S, Pandey S, Goel S. Plants disease identification and classification through leaf images: a survey. Arch Comput Methods Eng. 2019;26:507\u201330.","journal-title":"Arch Comput Methods Eng"},{"key":"9679_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2023.102011","volume":"75","author":"E Li","year":"2023","unstructured":"Li E, Wang L, Xie Q, Gao R, Su Z, Li Y. A novel deep learning method for maize disease identification based on small sample-size and complex background datasets. Eco Inform. 2023;75: 102011.","journal-title":"Eco Inform"},{"issue":"18","key":"9679_CR8","doi-asserted-by":"publisher","first-page":"13613","DOI":"10.1007\/s00500-022-07177-7","volume":"27","author":"A Chug","year":"2023","unstructured":"Chug A, Bhatia A, Singh AP, Singh D. A novel framework for image-based plant disease detection using hybrid deep learning approach. Soft Comput. 2023;27(18):13613\u201338.","journal-title":"Soft Comput"},{"key":"9679_CR9","first-page":"90","volume":"5","author":"P Bedi","year":"2021","unstructured":"Bedi P, Gole P. Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network. Artif Intell Agric. 2021;5:90\u2013101.","journal-title":"Artif Intell Agric"},{"key":"9679_CR10","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, Pandian A. Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Comput Electr Eng. 2019;76:323\u201338.","journal-title":"Comput Electr Eng"},{"key":"9679_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2020.103615","volume":"80","author":"R Sujatha","year":"2021","unstructured":"Sujatha R, Chatterjee JM, Jhanjhi N, Brohi SN. Performance of deep learning vs machine learning in plant leaf disease detection. Microprocess Microsyst. 2021;80: 103615.","journal-title":"Microprocess Microsyst"},{"key":"9679_CR12","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.biosystemseng.2018.05.013","volume":"172","author":"JG Barbedo","year":"2018","unstructured":"Barbedo JG. Factors influencing the use of deep learning for plant disease recognition. Biosys Eng. 2018;172:84\u201391.","journal-title":"Biosys Eng"},{"issue":"2","key":"9679_CR13","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1094\/PDIS-03-15-0340-FE","volume":"100","author":"A-K Mahlein","year":"2016","unstructured":"Mahlein A-K. Plant disease detection by imaging sensors-parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis. 2016;100(2):241\u201351.","journal-title":"Plant Dis"},{"issue":"4","key":"9679_CR14","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.3390\/agriengineering5040110","volume":"5","author":"SV Gudkov","year":"2023","unstructured":"Gudkov SV, Matveeva TA, Sarimov RM, Simakin AV, Stepanova EV, Moskovskiy MN, Dorokhov AS, Izmailov AY. Optical methods for the detection of plant pathogens and diseases. AgriEngineering. 2023;5(4):1789\u2013812.","journal-title":"AgriEngineering"},{"key":"9679_CR15","volume":"33","author":"M-L Huang","year":"2022","unstructured":"Huang M-L, Chuang T-C, Liao Y-C. Application of transfer learning and image augmentation technology for tomato pest identification. Sustain Comput Inf Syst. 2022;33: 100646.","journal-title":"Sustain Comput Inf Syst"},{"key":"9679_CR16","doi-asserted-by":"publisher","first-page":"18627","DOI":"10.1007\/s11042-020-08726-8","volume":"79","author":"MA Khan","year":"2020","unstructured":"Khan MA, Akram T, Sharif M, Javed K, Raza M, Saba T. An automated system for cucumber leaf diseased spot detection and classification using improved saliency method and deep features selection. Multimedia Tools Appl. 2020;79:18627\u201356.","journal-title":"Multimedia Tools Appl"},{"key":"9679_CR17","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.compag.2018.08.048","volume":"154","author":"J Ma","year":"2018","unstructured":"Ma J, Du K, Zheng F, Zhang L, Gong Z, Sun Z. A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Comput Electron Agric. 2018;154:18\u201324.","journal-title":"Comput Electron Agric"},{"key":"9679_CR18","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1007\/s11760-020-01797-y","volume":"15","author":"I El Massi","year":"2021","unstructured":"El Massi I, Es-saady Y, El Yassa M, Mammass D. Combination of multiple classifiers for automatic recognition of diseases and damages on plant leaves. SIViP. 2021;15:789\u201396.","journal-title":"SIViP"},{"key":"9679_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2021.107023","volume":"90","author":"G Hu","year":"2021","unstructured":"Hu G, Wang H, Zhang Y, Wan M. Detection and severity analysis of tea leaf blight based on deep learning. Comput Electr Eng. 2021;90: 107023.","journal-title":"Comput Electr Eng"},{"key":"9679_CR20","volume":"24","author":"G Hu","year":"2019","unstructured":"Hu G, Yang X, Zhang Y, Wan M. Identification of tea leaf diseases by using an improved deep convolutional neural network. Sustain Comput Inf Syst. 2019;24: 100353.","journal-title":"Sustain Comput Inf Syst"},{"key":"9679_CR21","volume":"30","author":"M Agarwal","year":"2021","unstructured":"Agarwal M, Gupta S, Biswas KK. A new conv2d model with modified relu activation function for identification of disease type and severity in cucumber plant. Sustain Comput Inf Syst. 2021;30: 100473.","journal-title":"Sustain Comput Inf Syst"},{"issue":"1","key":"9679_CR22","doi-asserted-by":"publisher","first-page":"6334","DOI":"10.1038\/s41598-022-10140-z","volume":"12","author":"MA Haque","year":"2022","unstructured":"Haque MA, Marwaha S, Deb CK, Nigam S, Arora A, Hooda KS, Soujanya PL, Aggarwal SK, Lall B, Kumar M, et al. Deep learning-based approach for identification of diseases of maize crop. Sci Rep. 2022;12(1):6334.","journal-title":"Sci Rep"},{"key":"9679_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2019.105093","volume":"167","author":"A Picon","year":"2019","unstructured":"Picon A, Seitz M, Alvarez-Gila A, Mohnke P, Ortiz-Barredo A, Echazarra J. Crop conditional convolutional neural networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions. Comput Electron Agric. 2019;167: 105093.","journal-title":"Comput Electron Agric"},{"key":"9679_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2020.101197","author":"RC Joshi","year":"2021","unstructured":"Joshi RC, Kaushik M, Dutta MK, Srivastava A, Choudhary N. Virleafnet: automatic analysis and viral disease diagnosis using deep-learning in vigna mungo plant. Eco Inform. 2021. https:\/\/doi.org\/10.1016\/j.ecoinf.2020.101197.","journal-title":"Eco Inform"},{"key":"9679_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2021.104027","author":"MB Tahir","year":"2021","unstructured":"Tahir MB, et al. Recognition of apple leaf diseases using deep learning and variances-controlled features reduction. Microprocess Microsyst. 2021. https:\/\/doi.org\/10.1016\/j.micpro.2021.104027.","journal-title":"Microprocess Microsyst"},{"key":"9679_CR26","volume":"35","author":"W Zeng","year":"2022","unstructured":"Zeng W, Li H, Hu G, Liang D. Identification of maize leaf diseases by using the skpsnet-50 convolutional neural network model. Sustain Comput Inf Syst. 2022;35: 100695.","journal-title":"Sustain Comput Inf Syst"},{"key":"9679_CR27","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/978-981-15-2449-3_11","volume-title":"Advances in intelligent systems and computing","author":"A Das","year":"2020","unstructured":"Das A, Mallick C, Dutta S. Deep learning-based automated feature engineering for rice leaf disease prediction. In: Advances in intelligent systems and computing, vol. 1120. Singapore: Springer; 2020. p. 133\u201341. https:\/\/doi.org\/10.1007\/978-981-15-2449-3_11."},{"key":"9679_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05235-5","author":"S U\u011fuz","year":"2020","unstructured":"U\u011fuz S, Uysal N. Classification of olive leaf diseases using deep convolutional neural networks. Neural Comput Appl. 2020. https:\/\/doi.org\/10.1007\/s00521-020-05235-5.","journal-title":"Neural Comput Appl"},{"key":"9679_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105456","volume":"175","author":"A Waheed","year":"2020","unstructured":"Waheed A, Goyal M, Gupta D, Khanna A, Hassanien AE, Pandey HM. An optimized dense convolutional neural network model for disease recognition and classification in corn leaf. Comput Electron Agric. 2020;175: 105456. https:\/\/doi.org\/10.1016\/j.compag.2020.105456.","journal-title":"Comput Electron Agric"},{"issue":"5","key":"9679_CR30","doi-asserted-by":"publisher","first-page":"2051","DOI":"10.18280\/TS.400523","volume":"40","author":"P Kalpana","year":"2023","unstructured":"Kalpana P, Anandan R. A capsule attention network for plant disease classification. Traitement du Signal. 2023;40(5):2051\u201362. https:\/\/doi.org\/10.18280\/TS.400523.","journal-title":"Traitement du Signal"},{"issue":"1","key":"9679_CR31","doi-asserted-by":"publisher","first-page":"8660","DOI":"10.1038\/s41598-024-56393-8","volume":"14","author":"P Kalpana","year":"2024","unstructured":"Kalpana P, Anandan R, Hussien AG, Migdady H, Abualigah L. Plant disease recognition using residual convolutional enlightened swin transformer networks. Sci Rep. 2024;14(1):8660. https:\/\/doi.org\/10.1038\/s41598-024-56393-8.","journal-title":"Sci Rep"},{"key":"9679_CR32","doi-asserted-by":"publisher","unstructured":"Kalpana P, Chanti Y, Ravi G, Regan D, Pareek PK. Se-resnet152 model: Early corn leaf disease identification and classification using feature based transfer learning technique. In: 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT) (2023). https:\/\/doi.org\/10.1109\/EASCT59475.2023.10392328.","DOI":"10.1109\/EASCT59475.2023.10392328"},{"key":"9679_CR33","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. 2020."},{"key":"9679_CR34","doi-asserted-by":"publisher","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B. Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9992\u201310002 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"9679_CR35","doi-asserted-by":"publisher","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S. End-to-end object detection with transformers. In: Computer Vision\u2013ECCV 2020. Lecture Notes in Computer Science, vol. 12346, pp. 213\u2013229. Springer, International Publishing (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"9679_CR36","unstructured":"Zhu X, Su W, Lu L, Li B, Wang X, Dai J. Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159. 2020."},{"key":"9679_CR37","doi-asserted-by":"publisher","DOI":"10.1145\/3505244","author":"S Khan","year":"2021","unstructured":"Khan S, Naseer M, Hayat M, Zamir SW, Khan FS, Shah M. Transformers in vision: a survey. ACM Comput Surv. 2021. https:\/\/doi.org\/10.1145\/3505244.","journal-title":"ACM Comput Surv"},{"issue":"4","key":"9679_CR38","doi-asserted-by":"publisher","first-page":"673","DOI":"10.3390\/agronomy14040673","volume":"14","author":"Z Chen","year":"2024","unstructured":"Chen Z, Wang G, Lv T, Zhang X. Using a hybrid convolutional neural network with a transformer model for tomato leaf disease detection. Agronomy. 2024;14(4):673.","journal-title":"Agronomy"},{"issue":"1","key":"9679_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12870-025-06679-4","volume":"25","author":"JH Sinamenye","year":"2025","unstructured":"Sinamenye JH, Chatterjee A, Shrestha R. Potato plant disease detection: leveraging hybrid deep learning models. BMC Plant Biol. 2025;25(1):1\u201315.","journal-title":"BMC Plant Biol"},{"key":"9679_CR40","unstructured":"Tonmoy MR, Hossain MM, Dey N, Mridha M. Mobileplantvit: a mobile-friendly hybrid vit for generalized plant disease image classification. arXiv preprint arXiv:2503.16628. 2025."},{"issue":"1","key":"9679_CR41","doi-asserted-by":"publisher","first-page":"270","DOI":"10.3390\/S25010270","volume":"25","author":"ET Baek","year":"2025","unstructured":"Baek ET. Attention score-based multi-vision transformer technique for plant disease classification. Sensors. 2025;25(1):270. https:\/\/doi.org\/10.3390\/S25010270.","journal-title":"Sensors"},{"issue":"1","key":"9679_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/S43621-024-00307-1","volume":"5","author":"W Ullah","year":"2024","unstructured":"Ullah W, et al. Efficient identification and classification of apple leaf diseases using lightweight vision transformer (vit). Discov Sustainabil. 2024;5(1):1\u201314. https:\/\/doi.org\/10.1007\/S43621-024-00307-1.","journal-title":"Discov Sustainabil"},{"issue":"17","key":"9679_CR43","doi-asserted-by":"publisher","first-page":"36361","DOI":"10.1016\/J.HELIYON.2024.E36361","volume":"10","author":"MA Hossain","year":"2024","unstructured":"Hossain MA, Sakib S, Abdullah HM, Arman SE. Deep learning for mango leaf disease identification: a vision transformer perspective. Heliyon. 2024;10(17):36361. https:\/\/doi.org\/10.1016\/J.HELIYON.2024.E36361.","journal-title":"Heliyon"},{"key":"9679_CR44","unstructured":"Chowdhury RH, Ahmed S. Mangoleafvit: leveraging lightweight vision transformer with runtime augmentation for efficient mango leaf disease classification. arXiv preprint arXiv:2505.23961, 20\u201322. 2025."},{"issue":"3","key":"9679_CR45","doi-asserted-by":"publisher","first-page":"339","DOI":"10.3390\/PLANTS14030339","volume":"14","author":"H Zhou","year":"2025","unstructured":"Zhou H, et al. A novel few-shot learning framework based on diffusion models for high-accuracy sunflower disease detection and classification. Plants. 2025;14(3):339. https:\/\/doi.org\/10.3390\/PLANTS14030339.","journal-title":"Plants"},{"key":"9679_CR46","doi-asserted-by":"publisher","first-page":"1280496","DOI":"10.3389\/FPLS.2023.1280496","volume":"14","author":"A Muhammad","year":"2023","unstructured":"Muhammad A, Salman Z, Lee K, Han D. Harnessing the power of diffusion models for plant disease image augmentation. Front Plant Sci. 2023;14:1280496. https:\/\/doi.org\/10.3389\/FPLS.2023.1280496.","journal-title":"Front Plant Sci"},{"key":"9679_CR47","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1007\/978-3-030-27202-9_10","volume":"11662","author":"S Mukherjee","year":"2020","unstructured":"Mukherjee S, Kottayil NK, Sun X, Cheng I. Cnn-based real-time parameter tuning for optimizing denoising filter performance. Lect Notes Comput Sci. 2020;11662:112\u201325. https:\/\/doi.org\/10.1007\/978-3-030-27202-9_10.","journal-title":"Lect Notes Comput Sci"},{"key":"9679_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105527","author":"PK Sethy","year":"2020","unstructured":"Sethy PK, Barpanda NK, Rath AK, Behera SK. Deep feature based rice leaf disease identification using support vector machine. Comput Electron Agric. 2020. https:\/\/doi.org\/10.1016\/j.compag.2020.105527.","journal-title":"Comput Electron Agric"},{"key":"9679_CR49","unstructured":"Gajjar R, Gajjar N, Thakor VJ, Patel NP, Ruparelia S. Real-time detection and identification of plant leaf diseases using convolutional neural networks on an embedded platform. Visual Comput 2022;1\u201316."},{"key":"9679_CR50","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. vol. 25. 2012. Accessed: Jun. 18, 2023. http:\/\/code.google.com\/p\/cuda-convnet\/."},{"issue":"7","key":"9679_CR51","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","volume":"26","author":"K Zhang","year":"2017","unstructured":"Zhang K, Zuo W, Chen Y, Meng D, Zhang L. Beyond a gaussian denoiser: residual learning of deep cnn for image denoising. IEEE Trans Image Process. 2017;26(7):3142\u201355. https:\/\/doi.org\/10.1109\/TIP.2017.2662206.","journal-title":"IEEE Trans Image Process"},{"key":"9679_CR52","doi-asserted-by":"publisher","first-page":"16591","DOI":"10.1109\/ACCESS.2021.3053408","volume":"9","author":"W Weng","year":"2015","unstructured":"Weng W, Zhu X. U-net: convolutional networks for biomedical image segmentation. IEEE Access. 2015;9:16591\u2013603. https:\/\/doi.org\/10.1109\/ACCESS.2021.3053408.","journal-title":"IEEE Access"},{"key":"9679_CR53","unstructured":"Goodfellow IJ, et al. Generative adversarial networks. Accessed: 06 Jun 2025 (2014). https:\/\/arxiv.org\/pdf\/1406.2661."},{"issue":"1\u20133","key":"9679_CR54","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"G-B Huang","year":"2006","unstructured":"Huang G-B, Zhu Q-Y, Siew C-K. Extreme learning machine: theory and applications. Neurocomputing. 2006;70(1\u20133):489\u2013501. https:\/\/doi.org\/10.1016\/j.neucom.2005.12.126.","journal-title":"Neurocomputing"},{"issue":"2","key":"9679_CR55","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","volume":"42","author":"G-B Huang","year":"2012","unstructured":"Huang G-B, Zhou H, Ding X, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybernet Part B (Cybernetics). 2012;42(2):513\u201329. https:\/\/doi.org\/10.1109\/TSMCB.2011.2168604.","journal-title":"IEEE Trans Syst Man Cybernet Part B (Cybernetics)"},{"key":"9679_CR56","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1016\/j.procs.2020.03.322","volume":"167","author":"S Dalal","year":"2020","unstructured":"Dalal S, Vishwakarma VP. Ga based kelm optimization for ecg classification. Proc Comput Sci. 2020;167:580\u20138. https:\/\/doi.org\/10.1016\/j.procs.2020.03.322.","journal-title":"Proc Comput Sci"},{"key":"9679_CR57","doi-asserted-by":"crossref","unstructured":"Zhang L, Zhang D, Tian F. Svm and elm: who wins? object recognition with deep convolutional features from imagenet. arXiv preprint arXiv:1506.02509, 249\u2013263 (2015) 10.48550\/arxiv.1506.02509.","DOI":"10.1007\/978-3-319-28397-5_20"},{"key":"9679_CR58","doi-asserted-by":"publisher","first-page":"1003","DOI":"10.1007\/978-981-32-9775-3_89","volume":"587","author":"VP Vishwakarma","year":"2020","unstructured":"Vishwakarma VP, Dalal S. A novel approach for compensation of light variation effects with kelm classification for efficient face recognition. Lecture Notes Electr Eng. 2020;587:1003\u201312. https:\/\/doi.org\/10.1007\/978-981-32-9775-3_89.","journal-title":"Lecture Notes Electr Eng"},{"key":"9679_CR59","unstructured":"He K, Zhang X, Ren S, Sun J. Deep Residual learning for image recognition. Accessed: 07 Jun 2025 (2015). http:\/\/arxiv.org\/abs\/1512.03385."},{"key":"9679_CR60","doi-asserted-by":"publisher","unstructured":"Ripley BD. Pattern recognition and neural networks. Cambridge University Press, (2014). https:\/\/doi.org\/10.1017\/CBO9780511812651.","DOI":"10.1017\/CBO9780511812651"},{"key":"9679_CR61","unstructured":"Bishop CM. Neural networks for pattern recognition. Oxford University Press, (1995). Accessed 26 Jul 2022. https:\/\/dl.acm.org\/doi\/book\/10.5555\/525960."},{"key":"9679_CR62","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016;2016:770\u20138. https:\/\/doi.org\/10.1109\/CVPR.2016.90.","DOI":"10.1109\/CVPR.2016.90"},{"key":"9679_CR63","unstructured":"spMohanty: PlantVillage-Dataset: dataset of diseased plant leaf images and corresponding labels. GitHub repository. Accessed 11 Dec 2020. https:\/\/github.com\/spMohanty\/PlantVillage-Dataset"},{"key":"9679_CR64","doi-asserted-by":"crossref","unstructured":"Patidar S, Pandey A, Shirish BA, Sriram A. Rice plant disease detection and classification using deep residual learning. In: International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, pp. 278\u2013293 (2020). Springer.","DOI":"10.1007\/978-981-15-6315-7_23"},{"key":"9679_CR65","volume":"35","author":"A Nasiri","year":"2022","unstructured":"Nasiri A, Omid M, Taheri-Garavand A, Jafari A. Deep learning-based precision agriculture through weed recognition in sugar beet fields. Sustain Comput Inf Syst. 2022;35: 100759.","journal-title":"Sustain Comput Inf Syst"},{"key":"9679_CR66","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106042","volume":"183","author":"J Shin","year":"2021","unstructured":"Shin J, Chang YK, Heung B, Nguyen-Quang T, Price GW, Al-Mallahi A. A deep learning approach for rgb image-based powdery mildew disease detection on strawberry leaves. Comput Electron Agric. 2021;183: 106042.","journal-title":"Comput Electron Agric"},{"key":"9679_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2019.104948","volume":"165","author":"K Kamal","year":"2019","unstructured":"Kamal K, Yin Z, Wu M, Wu Z. Depthwise separable convolution architectures for plant disease classification. Comput Electron Agric. 2019;165: 104948.","journal-title":"Comput Electron Agric"},{"key":"9679_CR68","volume":"28","author":"M Agarwal","year":"2020","unstructured":"Agarwal M, Gupta SK, Biswas KK. Development of efficient cnn model for tomato crop disease identification. Sustain Comput Inf Syst. 2020;28: 100407.","journal-title":"Sustain Comput Inf Syst"},{"key":"9679_CR69","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. Plant leaf disease classification using efficientnet deep learning model. Eco Inform. 2021;61: 101182.","journal-title":"Eco Inform"},{"key":"9679_CR70","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2021.101334","volume":"64","author":"S Kolhar","year":"2021","unstructured":"Kolhar S, Jagtap J. Spatio-temporal deep neural networks for accession classification of arabidopsis plants using image sequences. Eco Inform. 2021;64: 101334.","journal-title":"Eco Inform"},{"key":"9679_CR71","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2021.101247","volume":"61","author":"S Yadav","year":"2021","unstructured":"Yadav S, Sengar N, Singh A, Singh A, Dutta MK. Identification of disease using deep learning and evaluation of bacteriosis in peach leaf. Eco Inform. 2021;61: 101247.","journal-title":"Eco Inform"},{"key":"9679_CR72","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2021.101283","volume":"63","author":"B Gokulnath","year":"2021","unstructured":"Gokulnath B, et al. Identifying and classifying plant disease using resilient lf-cnn. Eco Inform. 2021;63: 101283.","journal-title":"Eco Inform"}],"container-title":["Discover Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09679-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10791-025-09679-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09679-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T16:30:33Z","timestamp":1757262633000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10791-025-09679-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,21]]},"references-count":72,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["9679"],"URL":"https:\/\/doi.org\/10.1007\/s10791-025-09679-y","relation":{},"ISSN":["2948-2992"],"issn-type":[{"value":"2948-2992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,21]]},"assertion":[{"value":"27 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interest.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"154"}}