{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T16:13:41Z","timestamp":1779898421899,"version":"3.53.1"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T00:00:00Z","timestamp":1699833600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T00:00:00Z","timestamp":1699833600000},"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\/s11042-023-17503-2","type":"journal-article","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T06:01:50Z","timestamp":1699855310000},"page":"18147-18168","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Evaluating the impact of tuned pre-trained architectures' feature maps on deep learning model performance for tomato disease detection"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3327-2822","authenticated-orcid":false,"given":"Halit","family":"Bak\u0131r","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,13]]},"reference":[{"key":"17503_CR1","doi-asserted-by":"crossref","unstructured":"Bak\u0131r H, \u00c7ay\u0131r AN, Navruz TS (2023) A comprehensive experimental study for analyzing the effects of data augmentation techniques on voice classification. Multimed Tools Appl 1\u201328","DOI":"10.1007\/s11042-023-16200-4"},{"issue":"6","key":"17503_CR2","doi-asserted-by":"publisher","DOI":"10.1088\/1402-4896\/acd4fa","volume":"98","author":"H Bak\u0131r","year":"2023","unstructured":"Bak\u0131r H, Elmabruk K (2023) Deep learning-based approach for detection of turbulence-induced distortions in free-space optical communication links. Phys Scr 98(6):065521","journal-title":"Phys Scr"},{"key":"17503_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2023.108804","volume":"110","author":"H Bak\u0131r","year":"2023","unstructured":"Bak\u0131r H, Bak\u0131r R (2023) DroidEncoder: Malware detection using auto-encoder based feature extractor and machine learning algorithms. Comput Electr Eng 110:108804","journal-title":"Comput Electr Eng"},{"issue":"4","key":"17503_CR4","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1007\/s42979-023-01798-x","volume":"4","author":"R Ghanem","year":"2023","unstructured":"Ghanem R, Erbay H, Bakour K (2023) Contents-based spam detection on social networks using RoBERTa embedding and stacked BLSTM. SN Comput Sci 4(4):380","journal-title":"SN Comput Sci"},{"key":"17503_CR5","doi-asserted-by":"crossref","unstructured":"Demircio\u011flu U, Sayil A, Bak\u0131r H (2023) Detecting cutout shape and predicting \u0131ts location in sandwich structures using free vibration analysis and tuned machine-learning algorithms. Arab J Sci Eng 1\u201314","DOI":"10.1007\/s13369-023-07917-3"},{"key":"17503_CR6","first-page":"419","volume":"052","author":"H Bakir","year":"2023","unstructured":"Bakir H, Oktay S, Tabaru E (2023) Detection of pneumonia from x-ray images using deep learning techniques. J Sci Rep A 052:419\u2013440","journal-title":"J Sci Rep A"},{"issue":"1","key":"17503_CR7","first-page":"17","volume":"1","author":"H Bakir","year":"2022","unstructured":"Bakir H, Yilmaz \u015e (2022) Using transfer learning technique as a feature extraction phase for diagnosis of cataract disease in the eye. Int J Sivas Univ Sci Technol 1(1):17\u201333","journal-title":"Int J Sivas Univ Sci Technol"},{"key":"17503_CR8","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/j.procs.2020.03.225","volume":"167","author":"M Agarwal","year":"2020","unstructured":"Agarwal M, Singh A, Arjaria S, Sinha A, Gupta S (2020) ToLeD: tomato leaf disease detection using convolution neural network. Procedia Comput Sci 167:293\u2013301","journal-title":"Procedia Comput Sci"},{"key":"17503_CR9","doi-asserted-by":"crossref","unstructured":"Ahmad I, Hamid M, Yousaf S, Shah ST, Ahmad MO (2020) Optimizing pretrained convolutional neural networks for tomato leaf disease detection. Complexity 2020 1\u20136","DOI":"10.1155\/2020\/8812019"},{"key":"17503_CR10","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","journal-title":"Appl Soft Comput"},{"issue":"2","key":"17503_CR11","doi-asserted-by":"publisher","first-page":"294","DOI":"10.3390\/agriengineering3020020","volume":"3","author":"MEH Chowdhury","year":"2021","unstructured":"Chowdhury MEH et al (2021) Automatic and reliable leaf disease detection using deep learning techniques. AgriEngineering 3(2):294\u2013312","journal-title":"AgriEngineering"},{"key":"17503_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105951","volume":"181","author":"V Gonzalez-Huitron","year":"2021","unstructured":"Gonzalez-Huitron V, Le\u00f3n-Borges JA, Rodriguez-Mata AE, Amabilis-Sosa LE, Ram\u00edrez-Pereda B, Rodriguez H (2021) Disease detection in tomato leaves via CNN with lightweight architectures implemented in raspberry pi 4. Comput Electron Agric 181:105951","journal-title":"Comput Electron Agric"},{"key":"17503_CR13","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","journal-title":"Comput Electron Agric"},{"issue":"7","key":"17503_CR14","doi-asserted-by":"publisher","first-page":"651","DOI":"10.3390\/agriculture11070651","volume":"11","author":"S Zhao","year":"2021","unstructured":"Zhao S, Peng Y, Liu J, Wu S (2021) Tomato leaf disease diagnosis based on improved convolution neural network by attention module. Agriculture 11(7):651","journal-title":"Agriculture"},{"key":"17503_CR15","doi-asserted-by":"publisher","first-page":"56607","DOI":"10.1109\/ACCESS.2020.2982456","volume":"8","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Song C, Zhang D (2020) Deep learning-based object detection improvement for tomato disease. IEEE Access 8:56607\u201356614","journal-title":"IEEE Access"},{"issue":"1","key":"17503_CR16","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1007\/s11277-020-07590-x","volume":"115","author":"J Basavaiah","year":"2020","unstructured":"Basavaiah J, Arlene Anthony A (2020) Tomato leaf disease classification using multiple feature extraction techniques. Wirel Pers Commun 115(1):633\u2013651","journal-title":"Wirel Pers Commun"},{"issue":"1","key":"17503_CR17","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/s41348-020-00403-0","volume":"128","author":"R Thangaraj","year":"2021","unstructured":"Thangaraj R, Anandamurugan S, Kaliappan VK (2021) Automated tomato leaf disease classification using transfer learning-based deep convolution neural network. J Plant Dis Prot 128(1):73\u201386","journal-title":"J Plant Dis Prot"},{"issue":"4","key":"17503_CR18","first-page":"566","volume":"7","author":"P Sharma","year":"2020","unstructured":"Sharma P, Berwal YPS, Ghai W (2020) Performance analysis of deep learning CNN models for disease detection in plants using image segmentation. Inf Process Agric 7(4):566\u2013574","journal-title":"Inf Process Agric"},{"issue":"3","key":"17503_CR19","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1007\/s41348-021-00465-8","volume":"129","author":"S Vallabhajosyula","year":"2022","unstructured":"Vallabhajosyula S, Sistla V, Kolli VKK (2022) Transfer learning-based deep ensemble neural network for plant leaf disease detection. J Plant Dis Prot 129(3):545\u2013558","journal-title":"J Plant Dis Prot"},{"issue":"3","key":"17503_CR20","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1007\/s41348-021-00528-w","volume":"129","author":"V ThanammalIndu","year":"2022","unstructured":"ThanammalIndu V, SujaPriyadharsini S (2022) Crossover-based wind-driven optimized convolutional neural network model for tomato leaf disease classification. J Plant Dis Protect 129(3):559\u2013578","journal-title":"J Plant Dis Protect"},{"issue":"12","key":"17503_CR21","doi-asserted-by":"publisher","first-page":"18583","DOI":"10.1007\/s11042-021-10599-4","volume":"80","author":"S Nandhini","year":"2021","unstructured":"Nandhini S, Ashokkumar K (2021) Improved crossover based monarch butterfly optimization for tomato leaf disease classification using convolutional neural network. Multimed Tools Appl 80(12):18583\u201318610","journal-title":"Multimed Tools Appl"},{"issue":"8","key":"17503_CR22","doi-asserted-by":"publisher","first-page":"2923","DOI":"10.1007\/s00371-021-02164-9","volume":"38","author":"R Gajjar","year":"2022","unstructured":"Gajjar R, Gajjar N, Thakor VJ, Patel NP, Ruparelia S (2022) Real-time detection and identification of plant leaf diseases using convolutional neural networks on an embedded platform. Vis Comput 38(8):2923\u20132938","journal-title":"Vis Comput"},{"key":"17503_CR23","unstructured":"Kaustubh B (2020) Tomato leaf disease detection. Kaggle.\u00a0https:\/\/www.kaggle.com\/datasets\/kaustubhb999\/tomatoleaf"},{"key":"17503_CR24","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.compag.2018.03.032","volume":"161","author":"EC Too","year":"2019","unstructured":"Too EC, Yujian L, Njuki S, Yingchun L (2019) A comparative study of fine-tuning deep learning models for plant disease identification. Comput Electron Agric 161:272\u2013279","journal-title":"Comput Electron Agric"},{"issue":"1","key":"17503_CR25","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1007\/s11277-020-07590-x","volume":"115","author":"J Basavaiah","year":"2020","unstructured":"Basavaiah J, Anthony AA (2020) Tomato leaf disease classification using multiple feature extraction techniques. Wirel Pers Commun 115(1):633\u2013651","journal-title":"Wirel Pers Commun"},{"key":"17503_CR26","doi-asserted-by":"crossref","unstructured":"Gajjar R, Gajjar N, Thakor VJ, Patel NP, Ruparelia S (2021) Real-time detection and identification of plant leaf diseases using convolutional neural networks on an embedded platform. Vis Comput 1\u201316","DOI":"10.1007\/s00371-021-02164-9"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17503-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-17503-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17503-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T09:11:43Z","timestamp":1706692303000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-17503-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,13]]},"references-count":26,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["17503"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-17503-2","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,13]]},"assertion":[{"value":"19 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}