{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T20:35:04Z","timestamp":1786048504915,"version":"3.56.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T00:00:00Z","timestamp":1711324800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T00:00:00Z","timestamp":1711324800000},"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\/s11042-024-18733-8","type":"journal-article","created":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T06:02:11Z","timestamp":1711346531000},"page":"4751-4771","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Deep feature-support vector machine based hybrid model for multi-crop leaf disease identification in Corn, Rice, and Wheat"],"prefix":"10.1007","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1339-520X","authenticated-orcid":false,"given":"Amit","family":"Bhola","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9945-7702","authenticated-orcid":false,"given":"Prabhat","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,25]]},"reference":[{"key":"18733_CR1","unstructured":"Nations U (2022) World\u2019s Population. https:\/\/www.un.org\/en\/global-issues\/population. Accessed 31 July 2023"},{"key":"18733_CR2","unstructured":"of Agriculture & Farmers\u00a0Welfare M (2023) Contribution of agricultural sector in GDP. https:\/\/www.pib.gov.in\/PressReleasePage.aspx?PRID=1909213. Accessed 31 July 2023"},{"key":"18733_CR3","unstructured":"Wire T (2023) Share of agriculture in employment rose, manufacturing declined In 2021-22: PLFS. https:\/\/thewire.in\/economy\/share-of-agriculture-in-employment-rose-manufacturing-declined-in-2021-22-plfs. Accessed 31 July 2023"},{"key":"18733_CR4","unstructured":"Today CA (2022) Pests and diseases cause worldwide damage to crops. https:\/\/californiaagtoday.com\/pests-diseases-cause-worldwide-damage-crops\/. Accessed 31 July 2023"},{"issue":"4","key":"18733_CR5","doi-asserted-by":"crossref","first-page":"2840","DOI":"10.1109\/JIOT.2021.3109019","volume":"10","author":"G Garg","year":"2023","unstructured":"Garg G, Gupta S, Mishra P, Vidyarthi A, Singh A, Ali A (2023) CROPCARE: an intelligent real-time sustainable IoT system for crop disease detection using mobile vision. IEEE Internet of Things J 10(4):2840\u20132851","journal-title":"IEEE Internet of Things J"},{"key":"18733_CR6","first-page":"88","volume":"23","author":"A Sinha","year":"2019","unstructured":"Sinha A, Shrivastava G, Kumar P (2019) Architecting user-centric internet of things for smart agriculture. Sustain Comput Inform Syst 23:88\u2013102","journal-title":"Sustain Comput Inform Syst"},{"key":"18733_CR7","doi-asserted-by":"crossref","unstructured":"Srinivas L, Bharathy AV, Ramakuri SK, Sethy A, Kumar R (2023) An optimized machine learning framework for crop disease detection. Multimed Tools Appl 1\u201320","DOI":"10.1007\/s11042-023-15446-2"},{"key":"18733_CR8","doi-asserted-by":"crossref","unstructured":"Bhola A, Kumar P (2023) Performance evaluation of different machine learning models in crop selection. In: Robotics, control and computer vision: select proceedings of ICRCCV 2022 pp 207\u2013217. Springer","DOI":"10.1007\/978-981-99-0236-1_16"},{"issue":"2","key":"18733_CR9","doi-asserted-by":"crossref","first-page":"1278","DOI":"10.1109\/TCBB.2022.3195291","volume":"20","author":"K Liu","year":"2022","unstructured":"Liu K, Zhang X (2022) PiTLiD: identification of plant disease from leaf images based on convolutional neural network. IEEE\/ACM Trans Comput Biol Bioinform 20(2):1278\u20131288","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"1","key":"18733_CR10","first-page":"23","volume":"13","author":"A Bhola","year":"2023","unstructured":"Bhola A, Verma S, Kumar P (2023) A comparative analysis of deep learning models for cucumber disease classification using transfer learning. J Current Sci Technol 13(1):23\u201335","journal-title":"J Current Sci Technol"},{"issue":"3","key":"18733_CR11","doi-asserted-by":"crossref","first-page":"2016","DOI":"10.1109\/TCBB.2022.3229114","volume":"20","author":"L Tian","year":"2023","unstructured":"Tian L, Zhang H, Liu B, Zhang J, Duan N, Yuan A, Huo Y (2023) VMF-SSD: a novel v-space based multi-scale feature fusion SSD for apple leaf disease detection. IEEE\/ACM Trans Comput Biol Bioinform 20(3):2016\u20132028","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"18733_CR12","unstructured":"Food of\u00a0the United\u00a0Nations AO, Dimensions of need - Staple foods: what do people eat? https:\/\/www.fao.org\/3\/u8480e\/u8480e07.htm. Accessed 10 July 2023"},{"issue":"6","key":"18733_CR13","doi-asserted-by":"crossref","first-page":"3506","DOI":"10.1016\/j.jksuci.2020.09.006","volume":"34","author":"S Khan","year":"2022","unstructured":"Khan S, Narvekar M (2022) Novel fusion of color balancing and superpixel based approach for detection of tomato plant diseases in natural complex environment. J King Saud University-Comput Inf Sci 34(6):3506\u20133516","journal-title":"J King Saud University-Comput Inf Sci"},{"issue":"6","key":"18733_CR14","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1049\/iet-ipr.2017.0822","volume":"12","author":"S Kaur","year":"2018","unstructured":"Kaur S, Pandey S, Goel S (2018) Semi-automatic leaf disease detection and classification system for soybean culture. IET Image Proc 12(6):1038\u20131048","journal-title":"IET Image Proc"},{"issue":"1","key":"18733_CR15","doi-asserted-by":"crossref","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":"7","key":"18733_CR16","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1049\/iet-cvi.2015.0414","volume":"10","author":"C Kalyoncu","year":"2016","unstructured":"Kalyoncu C, Toygar \u00d6 (2016) GTCLC: leaf classification method using multiple descriptors. IET Comput Vision 10(7):700\u2013708","journal-title":"IET Comput Vision"},{"key":"18733_CR17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2022\/1598796","volume":"2022","author":"AS Zamani","year":"2022","unstructured":"Zamani AS, Anand L, Rane KP, Prabhu P, Buttar AM, Pallathadka H, Raghuvanshi A, Dugbakie BN (2022) Performance of machine learning and image processing in plant leaf disease detection. J Food Qual 2022:1\u20137","journal-title":"J Food Qual"},{"issue":"6","key":"18733_CR18","doi-asserted-by":"crossref","first-page":"9431","DOI":"10.1007\/s11042-022-13715-0","volume":"82","author":"SU Rahman","year":"2023","unstructured":"Rahman SU, Alam F, Ahmad N, Arshad S (2023) Image processing based system for the detection, identification and treatment of tomato leaf diseases. Multimed Tool Appl 82(6):9431\u20139445","journal-title":"Multimed Tool Appl"},{"issue":"5","key":"18733_CR19","doi-asserted-by":"crossref","first-page":"13136","DOI":"10.1111\/exsy.13136","volume":"40","author":"I Ahmed","year":"2023","unstructured":"Ahmed I, Yadav PK (2023) Plant disease detection using machine learning approaches. Expert Syst 40(5):13136","journal-title":"Expert Syst"},{"issue":"1","key":"18733_CR20","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/s10044-022-01086-z","volume":"26","author":"M Prabu","year":"2023","unstructured":"Prabu M, Chelliah BJ (2023) An intelligent approach using boosted support vector machine based arithmetic optimization algorithm for accurate detection of plant leaf disease. Pattern Anal Appl 26(1):367\u2013379","journal-title":"Pattern Anal Appl"},{"issue":"12","key":"18733_CR21","doi-asserted-by":"crossref","first-page":"2047","DOI":"10.3390\/agriculture12122047","volume":"12","author":"Y Chen","year":"2022","unstructured":"Chen Y, Chen X, Lin J, Pan R, Cao T, Cai J, Yu D, Cernava T, Zhang X (2022) DFCANet: a novel lightweight convolutional neural network model for corn disease identification. Agriculture 12(12):2047","journal-title":"Agriculture"},{"key":"18733_CR22","doi-asserted-by":"crossref","unstructured":"Genaev M, Ekaterina S, Afonnikov D (2020) Application of neural networks to image recognition of wheat rust diseases. In: 2020 cognitive sciences, genomics and bioinformatics (CSGB). IEEE, pp 40\u201342","DOI":"10.1109\/CSGB51356.2020.9214703"},{"key":"18733_CR23","doi-asserted-by":"crossref","unstructured":"Faisal M, Leu J-S, Avian C, Prakosa SW, K\u00f6ppen M (2023) DFNet: dense fusion convolution neural network for plant leaf disease classification. Agron J","DOI":"10.1002\/agj2.21341"},{"issue":"14","key":"18733_CR24","doi-asserted-by":"crossref","first-page":"14628","DOI":"10.1109\/JSEN.2022.3182304","volume":"22","author":"J Chen","year":"2022","unstructured":"Chen J, Chen W, Zeb A, Yang S, Zhang D (2022) Lightweight inception networks for the recognition and detection of rice plant diseases. IEEE Sensors J 22(14):14628\u201314638","journal-title":"IEEE Sensors J"},{"key":"18733_CR25","doi-asserted-by":"crossref","first-page":"106468","DOI":"10.1016\/j.compag.2021.106468","volume":"190","author":"D Wang","year":"2021","unstructured":"Wang D, Wang J, Li W, Guan P (2021) T-CNN: trilinear convolutional neural networks model for visual detection of plant diseases. Comput Electron Agric 190:106468","journal-title":"Comput Electron Agric"},{"issue":"2","key":"18733_CR26","doi-asserted-by":"crossref","first-page":"1156","DOI":"10.1109\/TCBB.2022.3191854","volume":"20","author":"X Zhu","year":"2022","unstructured":"Zhu X, Li J, Jia R, Liu B, Yao Z, Yuan A, Huo Y, Zhang H (2022) LAD-Net: a novel light weight model for early apple leaf pests and diseases classification. IEEE\/ACM Trans Comput Biol Bioinform 20(2):1156\u20131169","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"3","key":"18733_CR27","first-page":"293","volume":"13","author":"N Ganatra","year":"2020","unstructured":"Ganatra N, Patel A (2020) Performance analysis of fine-tuned convolutional neural network models for plant disease classification. Int J Autom Control 13(3):293\u2013305","journal-title":"Int J Autom Control"},{"key":"18733_CR28","first-page":"23","volume":"6","author":"AS Paymode","year":"2022","unstructured":"Paymode AS, Malode VB (2022) Transfer learning for multi-crop leaf disease image classification using convolutional neural network VGG. Artif Intell Agric 6:23\u201333","journal-title":"Artif Intell Agric"},{"key":"18733_CR29","doi-asserted-by":"crossref","first-page":"28822","DOI":"10.1109\/ACCESS.2021.3058947","volume":"9","author":"C Zhou","year":"2021","unstructured":"Zhou C, Zhou S, Xing J, Song J (2021) Tomato leaf disease identification by restructured deep residual dense network. IEEE Access 9:28822\u201328831","journal-title":"IEEE Access"},{"key":"18733_CR30","doi-asserted-by":"crossref","unstructured":"Verma S, Kumar P, Singh JP (2023) A unified lightweight CNN-based model for disease detection and identification in Corn, Rice, and Wheat. IETE J Res 1\u201312","DOI":"10.1080\/03772063.2023.2181229"},{"issue":"1","key":"18733_CR31","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1007\/s10661-022-10656-x","volume":"195","author":"A Haridasan","year":"2023","unstructured":"Haridasan A, Thomas J, Raj ED (2023) Deep learning system for paddy plant disease detection and classification. Environ Monit Assess 195(1):120","journal-title":"Environ Monit Assess"},{"issue":"12","key":"18733_CR32","doi-asserted-by":"crossref","first-page":"18799","DOI":"10.1007\/s11042-022-14272-2","volume":"82","author":"N Aishwarya","year":"2023","unstructured":"Aishwarya N, Praveena N, Priyanka S, Pramod J (2023) Smart farming for detection and identification of tomato plant diseases using light weight deep neural network. Multimed Tool Appl 82(12):18799\u201318810","journal-title":"Multimed Tool Appl"},{"key":"18733_CR33","unstructured":"Vieira G, GitHub - gabrieldgf4\/PlantVillage-Dataset. https:\/\/github.com\/gabrieldgf4\/PlantVillage-Dataset. Accessed 10 July 2023"},{"key":"18733_CR34","doi-asserted-by":"crossref","unstructured":"Singh D, Jain N, Jain P, Kayal P, Kumawat S, Batra N (2020) PlantDoc: a dataset for visual plant disease detection. In: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD. pp 249\u2013253","DOI":"10.1145\/3371158.3371196"},{"issue":"10","key":"18733_CR35","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.3390\/plants9101319","volume":"9","author":"MH Saleem","year":"2020","unstructured":"Saleem MH, Potgieter J, Arif KM (2020) Plant disease classification: a comparative evaluation of convolutional neural networks and deep learning optimizers. Plants 9(10):1319","journal-title":"Plants"},{"key":"18733_CR36","doi-asserted-by":"crossref","first-page":"43721","DOI":"10.1109\/ACCESS.2019.2907383","volume":"7","author":"UP Singh","year":"2019","unstructured":"Singh UP, Chouhan SS, Jain S, Jain S (2019) Multilayer convolution neural network for the classification of mango leaves infected by anthracnose disease. IEEE access 7:43721\u201343729","journal-title":"IEEE access"},{"key":"18733_CR37","doi-asserted-by":"crossref","first-page":"105899","DOI":"10.1016\/j.engappai.2023.105899","volume":"120","author":"Y Akkem","year":"2023","unstructured":"Akkem Y, Biswas SK, Varanasi A (2023) Smart farming using artificial intelligence: a review. Eng Appl Artif Intell 120:105899","journal-title":"Eng Appl Artif Intell"},{"issue":"2","key":"18733_CR38","doi-asserted-by":"crossref","first-page":"352","DOI":"10.3390\/agriculture13020352","volume":"13","author":"H Orchi","year":"2023","unstructured":"Orchi H, Sadik M, Khaldoun M, Sabir E (2023) Automation of crop disease detection through conventional machine learning and deep transfer learning approaches. Agriculture 13(2):352","journal-title":"Agriculture"},{"key":"18733_CR39","doi-asserted-by":"crossref","unstructured":"Akkem Y, Biswas SK, Varanasi A (2023) Smart farming monitoring using ML and MLOps. In: International conference on innovative computing and communication. Springer, pp 665\u2013675","DOI":"10.1007\/978-981-99-3315-0_51"},{"key":"18733_CR40","unstructured":"RIYAZ S, Rice Leafs. https:\/\/www.kaggle.com\/datasets\/shayanriyaz\/riceleafs. Accessed 10 July 2023"},{"key":"18733_CR41","unstructured":"GETCH O, Wheat Leaf dataset. https:\/\/www.kaggle.com\/datasets\/olyadgetch\/wheat-leaf-dataset. Accessed 10 July 2023"},{"key":"18733_CR42","unstructured":"HUSSAIN S , CIGAR computer vision for crop disease. https:\/\/www.kaggle.com\/datasets\/shadabhussain\/cgiar-computer-vision-for-crop-disease. Accessed 10 July 2023"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18733-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18733-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18733-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,23]],"date-time":"2025-03-23T00:19:30Z","timestamp":1742689170000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18733-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,25]]},"references-count":42,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["18733"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18733-8","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,25]]},"assertion":[{"value":"4 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 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 authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}}]}}