{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T11:46:00Z","timestamp":1780919160760,"version":"3.54.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T00:00:00Z","timestamp":1689811200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T00:00:00Z","timestamp":1689811200000},"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\/s11042-023-16125-y","type":"journal-article","created":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T07:02:16Z","timestamp":1689836536000},"page":"17025-17045","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A novel deep neural network model using network deconvolution with attention based activation for crop disease classification"],"prefix":"10.1007","volume":"83","author":[{"given":"Nayan Kumar","family":"Sarkar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7314-9645","authenticated-orcid":false,"given":"Moirangthem Marjit","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Utpal","family":"Nandi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,20]]},"reference":[{"key":"16125_CR1","doi-asserted-by":"publisher","unstructured":"Abou El-Maged LM, Darwish A, Hassanien AE (2020) Artificial intelligence-based plant\u2019s diseases classification. In Aboul-Ella Hassanien, Ahmad Taher Azar, Tarek Gaber, Diego Oliva, and Fahmy M. Tolba, editors, Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). Advances in Intelligent Systems and Computing, vol 1153, pages 3\u201315, Cham, 2020. Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-030-44289-71","DOI":"10.1007\/978-3-030-44289-71"},{"key":"16125_CR2","doi-asserted-by":"publisher","first-page":"100407","DOI":"10.1016\/j.suscom.2020.100407","volume":"28","author":"M Agarwal","year":"2020","unstructured":"Agarwal M, Kr S, Gupta KKB (2020) Development of efficient cnn model for tomato crop disease identification. Sustain Comput Inf Syst 28:100407. https:\/\/doi.org\/10.1016\/j.suscom.2020.100407","journal-title":"Sustain Comput Inf Syst"},{"issue":"1","key":"16125_CR3","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(1):507\u2013524. https:\/\/doi.org\/10.1007\/s40747-021-00536-1","journal-title":"Complex Intell Syst"},{"issue":"6","key":"16125_CR4","doi-asserted-by":"publisher","first-page":"8925","DOI":"10.1016\/j.matpr.2021.05.584","volume":"78","author":"KS Archana","year":"2022","unstructured":"Archana KS, Srinivasan S, PrasannaBharathi S, Balamurugan R, Prabakar TN, Britto A (2022) A novel method to improve computational and classification performance of rice plant disease identification. JSupercompu 78(6):8925\u20138945. https:\/\/doi.org\/10.1016\/j.matpr.2021.05.584","journal-title":"JSupercompu"},{"key":"16125_CR5","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1016\/j.matpr.2021.05.584","volume":"51","author":"S Ashwinkumar","year":"2022","unstructured":"Ashwinkumar S, Rajagopal S, Manimaran V, Jegajothi B (2022) Automated plant leaf disease detection and classification using optimal mobilenet based convolutional neural networks. Mater Today Proc 51:480\u2013487","journal-title":"Mater Today Proc"},{"key":"16125_CR6","unstructured":"Bruinsma J et al. (2009) The resource outlook to 2050: by how much do land, water and crop yields need to increase by 2050? How to feed the World in 2050. Proceedings of a technical meeting of experts, Rome, Italy, 24\u201326 June 2009, pp 1\u201333"},{"key":"16125_CR7","doi-asserted-by":"publisher","first-page":"105393","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":"16125_CR8","doi-asserted-by":"publisher","unstructured":"Chen D, Li J, Xu K (2020) Arelu: Attention-based rectified linear unit.\u00a0https:\/\/doi.org\/10.48550\/arXiv.2006.13858","DOI":"10.48550\/arXiv.2006.13858"},{"issue":"6","key":"16125_CR9","doi-asserted-by":"publisher","first-page":"951","DOI":"10.3390\/electronics11060951","volume":"11","author":"H-C Chen","year":"2022","unstructured":"Chen H-C, Widodo AM, Wisnujati A, Rahaman M, Lin JC-W, Chen L, Weng C-E (2022) Alexnet convolutional neural network for disease detection and classification of tomato leaf. Electronics 11(6):951. https:\/\/doi.org\/10.3390\/electronics11060951","journal-title":"Electronics"},{"key":"16125_CR10","doi-asserted-by":"publisher","unstructured":"Durmus H, G\u00fcnes, EO, Kirc\u0131 M (2017) Disease detection on the leaves of the tomato plants by using deep learning. 2017 6th International Conference on Agro-Geoinformatics, Fairfax, VA, USA, pp. 1\u20135. https:\/\/doi.org\/10.1109\/Agro-Geoinformatics.2017.8047016","DOI":"10.1109\/Agro-Geoinformatics.2017.8047016"},{"key":"16125_CR11","doi-asserted-by":"publisher","first-page":"9471","DOI":"10.1109\/ACCESS.2022.3142817","volume":"10","author":"E Elfatimi","year":"2022","unstructured":"Elfatimi E, Eryigit R, Elfatimi L (2022) Beans leaf diseases classification using mobilenet models. IEEE Access 10:9471\u20139482. https:\/\/doi.org\/10.1109\/ACCESS.2022.3142817","journal-title":"IEEE Access"},{"issue":"9","key":"16125_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s17092022","volume":"17","author":"A Fuentes","year":"2017","unstructured":"Fuentes A, Yoon S, Kim SC, Park DS (2017) A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17(9):1\u201321. https:\/\/doi.org\/10.3390\/s17092022","journal-title":"Sensors"},{"key":"16125_CR13","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 (2019) Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Comput Electric Eng 76:323\u2013338. https:\/\/doi.org\/10.1016\/j.compeleceng.2019.04.011","journal-title":"Comput Electric Eng"},{"key":"16125_CR14","doi-asserted-by":"publisher","first-page":"5390","DOI":"10.1109\/ACCESS.2022.3141371","volume":"10","author":"SM Hassan","year":"2022","unstructured":"Hassan SM, Maji AK (2022) Plant disease identification using a novel convolutional neural network. IEEE Access 10:5390\u20135401. https:\/\/doi.org\/10.1109\/ACCESS.2022.3141371","journal-title":"IEEE Access"},{"key":"16125_CR15","doi-asserted-by":"publisher","unstructured":"Jaisakthi SM, Mirunalini P, Thenmozhi D, Vatsala (2019) Grape leaf disease identification using machine learning techniques. In 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), Chennai, India, pp 1\u20136. https:\/\/doi.org\/10.1109\/ICCIDS.2019.8862084","DOI":"10.1109\/ICCIDS.2019.8862084"},{"issue":"3","key":"16125_CR16","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1016\/j.inpa.2019.10.003","volume":"7","author":"M Ji","year":"2020","unstructured":"Ji M, Zhang L, Qiufeng W (2020) Automatic grape leaf diseases identification via united model based on multiple convolutional neural networks. Inf Process Agric 7(3):418\u2013426. https:\/\/doi.org\/10.1016\/j.inpa.2019.10.003","journal-title":"Inf Process Agric"},{"key":"16125_CR17","doi-asserted-by":"publisher","unstructured":"Joachims T (1999) Making large scale svm learning practical. Reposit TU Dortmund, pp 1\u201313.\u00a0https:\/\/doi.org\/10.17877\/DE290R-5098","DOI":"10.17877\/DE290R-5098"},{"key":"16125_CR18","doi-asserted-by":"publisher","first-page":"105933","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":"16125_CR19","doi-asserted-by":"publisher","unstructured":"Kaur M, Bhatia R (2019) Development of an improved tomato leaf disease detection and classification method. 2019 IEEE Conf Inf Comm Technol, Allahabad, India, pp 1\u20135.\u00a0https:\/\/doi.org\/10.1109\/CICT48419.2019.9066230","DOI":"10.1109\/CICT48419.2019.9066230"},{"key":"16125_CR20","doi-asserted-by":"publisher","unstructured":"Khalifa NEM, Taha MHN, Abou El-Maged LM, Hassanien AE (2021) Artificial intelligence in potato leaf disease classification: A deep learning approach. In Aboul Ella Hassanien and Ashraf Darwish, editors, Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges. Studies in Big Data, vol 77, pages 63\u201379, Cham, 2021. Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-030-59338-44","DOI":"10.1007\/978-3-030-59338-44"},{"key":"16125_CR21","doi-asserted-by":"publisher","first-page":"105803","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"},{"issue":"5","key":"16125_CR22","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/S0167-4048(02)00514-X","volume":"21","author":"Y Liao","year":"2002","unstructured":"Liao Y, Vemuri VR (2002) Use of k-nearest neighbor classifier for intrusion detection. Comput Sec 21(5):439\u2013448. https:\/\/doi.org\/10.1016\/S0167-4048(02)00514-X","journal-title":"Comput Sec"},{"key":"16125_CR23","doi-asserted-by":"publisher","first-page":"04","DOI":"10.3389\/fpls.2016.01419","volume":"7","author":"S Mohanty","year":"2016","unstructured":"Mohanty S, Hughes D, Salathe M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:04. https:\/\/doi.org\/10.3389\/fpls.2016.01419","journal-title":"Front Plant Sci"},{"issue":"8","key":"16125_CR24","doi-asserted-by":"publisher","first-page":"1266","DOI":"10.3390\/electronics11081266","volume":"11","author":"JA Pandian","year":"2022","unstructured":"Pandian JA, Kanchanadevi K, Kumar VD, Jasinska E, Gono R, Leonowicz Z, Jasinski M (2022) A five convolutional layer deep convolutional neural network for plant leaf disease detection. Electronics 11(8):1266. https:\/\/doi.org\/10.3390\/electronics11081266","journal-title":"Electronics"},{"key":"16125_CR25","doi-asserted-by":"publisher","first-page":"98716","DOI":"10.1109\/ACCESS.2020.2997001","volume":"8","author":"W Qiufeng","year":"2020","unstructured":"Qiufeng W, Chen Y, Meng J (2020) Dcgan-based data augmentation for tomato leaf disease identification. IEEE Access 8:98716\u201398728. https:\/\/doi.org\/10.1109\/ACCESS.2020.2997001","journal-title":"IEEE Access"},{"key":"16125_CR26","doi-asserted-by":"publisher","first-page":"1040","DOI":"10.1016\/j.procs.2018.07.070","volume":"133","author":"AK Rangarajan","year":"2018","unstructured":"Rangarajan AK, Purushothaman R, Ramesh A (2018) Tomato crop disease classification using pre-trained deep learning algorithm. Proc Comput Sci 133:1040\u20131047. https:\/\/doi.org\/10.1016\/j.procs.2018.07.070","journal-title":"Proc Comput Sci"},{"key":"16125_CR27","doi-asserted-by":"publisher","first-page":"08","DOI":"10.3390\/electronics10172064","volume":"10","author":"J Rashid","year":"2021","unstructured":"Rashid J, Khan I, Ghulam A, Almotiri S, Alghamdi M, Masood K (2021) Multi-level deep learning model for potato leaf disease recognition. Electronics 10:08. https:\/\/doi.org\/10.3390\/electronics10172064","journal-title":"Electronics"},{"key":"16125_CR28","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/0-387-25465-X9","volume-title":"Data mining and knowledge discovery handbook","author":"L Rokach","year":"2005","unstructured":"Rokach L, Maimon O (2005) Decision trees. In: Maimon O, Rokach L (eds) Data mining and knowledge discovery handbook. Springer, Boston, MA, pp 165\u2013192. https:\/\/doi.org\/10.1007\/0-387-25465-X9"},{"key":"16125_CR29","doi-asserted-by":"publisher","first-page":"107164","DOI":"10.1016\/j.asoc.2021.107164","volume":"103","author":"F Saeed","year":"2021","unstructured":"Saeed F, Khan M, Sharif M, Mittal M, Goyal L, Roy S (2021) Deep neural network features fusion and selection based on pls regression with an application for crops diseases classification. Appl Soft Comput 103:107164. https:\/\/doi.org\/10.1016\/j.asoc.2021.107164","journal-title":"Appl Soft Comput"},{"issue":"1","key":"16125_CR30","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1007\/s10489-021-02452-w","volume":"52","author":"SF Syed-Ab-Rahman","year":"2022","unstructured":"Syed-Ab-Rahman SF, Hesamian MH, Prasad M (2022) Citrus disease detection and classification using end-to-end anchor-based deep learning model. Appl Intell 52(1):927\u2013938. https:\/\/doi.org\/10.1007\/s10489-021-02452-w","journal-title":"Appl Intell"},{"key":"16125_CR31","doi-asserted-by":"publisher","unstructured":"Szegedy C et al (2015) Going deeper with convolutions. In Boston, MA, and USA, editors, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1\u20139. https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"16125_CR32","doi-asserted-by":"publisher","first-page":"105735","DOI":"10.1016\/j.compag.2020.105735","volume":"178","author":"Z Tang","year":"2020","unstructured":"Tang Z, Yang J, Li Z, Qi F (2020) Grape disease image classification based on lightweight convolution neural networks and channelwise attention. Comput Electron Agric 178:105735. https:\/\/doi.org\/10.1016\/j.compag.2020.105735","journal-title":"Comput Electron Agric"},{"issue":"1","key":"16125_CR33","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 Protect 128(1):73\u201386. https:\/\/doi.org\/10.1007\/s41348-020-00403-0","journal-title":"J Plant Dis Protect"},{"key":"16125_CR34","doi-asserted-by":"publisher","first-page":"104906","DOI":"10.1016\/j.compag.2019.104906","volume":"164","author":"K Thenmozhi","year":"2019","unstructured":"Thenmozhi K, Srinivasulu Reddy U (2019) Crop pest classification based on deep convolutional neural network and transfer learning. Comput Electron Agric 164:104906. https:\/\/doi.org\/10.1016\/j.compag.2019.104906","journal-title":"Comput Electron Agric"},{"key":"16125_CR35","unstructured":"Vedaldi A, Zisserman A (2016) Vgg convolutional neural networks practical. Department of Engineering Science, University of Oxford, 66"},{"key":"16125_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fpls.2020.600854","volume":"11","author":"G Yang","year":"2020","unstructured":"Yang G, He Y, Yang Y, Beibei X (2020) Fine-grained image classification for crop disease based on attention mechanism. Front Plant Sci 11:1\u201315. https:\/\/doi.org\/10.3389\/fpls.2020.600854","journal-title":"Front Plant Sci"},{"key":"16125_CR37","doi-asserted-by":"publisher","unstructured":"Ye C, Evanusa M, He H, Mitrokhin A, Goldstein T, Yorke JA, Ferm\u00fcller C, Aloimonos Y (2019) Network deconvolution.\u00a0https:\/\/doi.org\/10.48550\/arXiv.1905.11926","DOI":"10.48550\/arXiv.1905.11926"},{"key":"16125_CR38","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/978-3-030-03335-440","volume":"11257","author":"Y Yuan","year":"2018","unstructured":"Yuan Y, Fang S, Chen L (2018) Crop disease image classification based on transfer learning with dcnns. In Chinese Conference on Pattern Recognition and Computer Vision (PRCV) 11257:457\u2013468. https:\/\/doi.org\/10.1007\/978-3-030-03335-440","journal-title":"In Chinese Conference on Pattern Recognition and Computer Vision (PRCV)"},{"key":"16125_CR39","doi-asserted-by":"publisher","unstructured":"Yuan Z-W, Zhang J (2016) Feature extraction and image retrieval based on AlexNet. In Charles M. Falco and Xudong Jiang, editors, Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), pp 10033.\u00a0https:\/\/doi.org\/10.1117\/12.2243849","DOI":"10.1117\/12.2243849"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16125-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16125-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16125-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T08:53:09Z","timestamp":1706691189000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16125-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,20]]},"references-count":39,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["16125"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16125-y","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,20]]},"assertion":[{"value":"30 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 May 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 June 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 July 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 approve that the research presented in this paper is conducted following the principles of ethical and professional conduct.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable, the authors used publicly available data only and provide the corresponding references.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"There is no conflicts of interest\/competing interest.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}