{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T07:48:23Z","timestamp":1769586503042,"version":"3.49.0"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"26","license":[{"start":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:00:00Z","timestamp":1744156800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:00:00Z","timestamp":1744156800000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s00521-025-11179-5","type":"journal-article","created":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T18:52:53Z","timestamp":1744224773000},"page":"22141-22160","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An efficient deep learning model for early disease detection in vegetable crops"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1339-520X","authenticated-orcid":false,"given":"Amit","family":"Bhola","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prabhat","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,9]]},"reference":[{"key":"11179_CR1","unstructured":"(APEDA) A.P.F.P.E.D.A. Fresh Fruits & Vegetables. https:\/\/apeda.gov.in\/apedawebsite\/six_head_product\/FFV.htm. Accessed 15 March 2024"},{"key":"11179_CR2","doi-asserted-by":"publisher","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":"8","key":"11179_CR3","doi-asserted-by":"publisher","first-page":"5963","DOI":"10.1007\/s00521-022-07951-6","volume":"35","author":"S Chulif","year":"2023","unstructured":"Chulif S, Lee SH, Chang YL, Chai KC (2023) A machine learning approach for cross-domain plant identification using herbarium specimens. Neural Comput Appl 35(8):5963\u20135985","journal-title":"Neural Comput Appl"},{"issue":"5","key":"11179_CR4","doi-asserted-by":"publisher","first-page":"4111","DOI":"10.12694\/scpe.v25i5.2599","volume":"25","author":"A Bhola","year":"2024","unstructured":"Bhola A, Kumar P (2024) Ml-csfr: a unified crop selection and fertilizer recommendation framework based on machine learning. Scalable Comput Pract Exp 25(5):4111\u20134127","journal-title":"Scalable Comput Pract Exp"},{"issue":"16","key":"11179_CR5","doi-asserted-by":"publisher","first-page":"13951","DOI":"10.1007\/s00521-022-07246-w","volume":"34","author":"M Subramanian","year":"2022","unstructured":"Subramanian M, Shanmugavadivel K, Nandhini P (2022) On fine-tuning deep learning models using transfer learning and hyper-parameters optimization for disease identification in maize leaves. Neural Comput Appl 34(16):13951\u201313968","journal-title":"Neural Comput Appl"},{"key":"11179_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11042-024-18733-8","volume":"84","author":"A Bhola","year":"2024","unstructured":"Bhola A, Kumar P (2024) Deep feature-support vector machine based hybrid model for multi-crop leaf disease identification in corn, rice, and wheat. Multimed Tools Appl 84:1\u201321","journal-title":"Multimed Tools Appl"},{"issue":"4","key":"11179_CR7","doi-asserted-by":"publisher","first-page":"2840","DOI":"10.1109\/JIOT.2021.3109019","volume":"10","author":"G Garg","year":"2021","unstructured":"Garg G, Gupta S, Mishra P, Vidyarthi A, Singh A, Ali A (2021) Cropcare: an intelligent real-time sustainable iot system for crop disease detection using mobile vision. IEEE Internet Things J 10(4):2840\u20132851","journal-title":"IEEE Internet Things J"},{"key":"11179_CR8","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":"11179_CR9","doi-asserted-by":"publisher","first-page":"107881","DOI":"10.1016\/j.engappai.2024.107881","volume":"131","author":"Y Akkem","year":"2024","unstructured":"Akkem Y, Biswas SK, Varanasi A (2024) A comprehensive review of synthetic data generation in smart farming by using variational autoencoder and generative adversarial network. Eng Appl Artif Intell 131:107881","journal-title":"Eng Appl Artif Intell"},{"key":"11179_CR10","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1016\/j.future.2023.06.016","volume":"148","author":"H-T Thai","year":"2023","unstructured":"Thai H-T, Le K-H, Nguyen NL-T (2023) Towards sustainable agriculture: A lightweight hybrid model and cloud-based collection of datasets for efficient leaf disease detection. Futur Gener Comput Syst 148:488\u2013500","journal-title":"Futur Gener Comput Syst"},{"issue":"7","key":"11179_CR11","doi-asserted-by":"publisher","first-page":"3255","DOI":"10.1007\/s11760-023-02498-y","volume":"17","author":"R Bora","year":"2023","unstructured":"Bora R, Parasar D, Charhate S (2023) A detection of tomato plant diseases using deep learning mndlnn classifier. SIViP 17(7):3255\u20133263","journal-title":"SIViP"},{"issue":"6","key":"11179_CR12","doi-asserted-by":"publisher","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 Tools Appl 82(6):9431\u20139445","journal-title":"Multimed Tools Appl"},{"key":"11179_CR13","doi-asserted-by":"crossref","unstructured":"Gupta S, Gilotra S, Rathi S, Choudhury T, Kotecha K (2024) Plant disease recognition using different cnn models. In: 2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence), IEEE pp 787\u2013792","DOI":"10.1109\/Confluence60223.2024.10463383"},{"key":"11179_CR14","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 Proceed 51:480\u2013487","journal-title":"Mater Today Proceed"},{"issue":"48","key":"11179_CR15","doi-asserted-by":"publisher","first-page":"4688","DOI":"10.17485\/IJST\/v16i48.2850","volume":"16","author":"Y Akkem","year":"2023","unstructured":"Akkem Y, Kumar B, Varanasi A (2023) Streamlit application for advanced ensemble learning methods in crop recommendation systems-a review and implementation. Indian J Sci Technol 16(48):4688\u20134702","journal-title":"Indian J Sci Technol"},{"key":"11179_CR16","first-page":"1","volume":"12","author":"A Bhola","year":"2024","unstructured":"Bhola A, Kumar P (2024) Farm-level smart crop recommendation framework using machine learning. Ann Data Sci 12:1\u201324","journal-title":"Ann Data Sci"},{"issue":"1","key":"11179_CR17","first-page":"11","volume":"10","author":"LC Ngugi","year":"2023","unstructured":"Ngugi LC, Abdelwahab M, Abo-Zahhad M (2023) A new approach to learning and recognizing leaf diseases from individual lesions using convolutional neural networks. Inform Process Agric 10(1):11\u201327","journal-title":"Inform Process Agric"},{"key":"11179_CR18","doi-asserted-by":"publisher","first-page":"100313","DOI":"10.1016\/j.array.2023.100313","volume":"19","author":"SG Paul","year":"2023","unstructured":"Paul SG, Biswas AA, Saha A, Zulfiker MS, Ritu NA, Zahan I, Rahman M, Islam MA (2023) A real-time application-based convolutional neural network approach for tomato leaf disease classification. Array 19:100313","journal-title":"Array"},{"issue":"9","key":"11179_CR19","doi-asserted-by":"publisher","first-page":"12407","DOI":"10.1007\/s12652-022-04331-9","volume":"14","author":"P Kaur","year":"2023","unstructured":"Kaur P, Harnal S, Gautam V, Singh MP, Singh SP (2023) A novel transfer deep learning method for detection and classification of plant leaf disease. J Ambient Intell Humaniz Comput 14(9):12407\u201312424","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"1","key":"11179_CR20","first-page":"1","volume":"10","author":"R Yang","year":"2023","unstructured":"Yang R, Wu Z, Fang W, Zhang H, Wang W, Fu L, Majeed Y, Li R, Cui Y (2023) Detection of abnormal hydroponic lettuce leaves based on image processing and machine learning. Inform Process Agric 10(1):1\u201310","journal-title":"Inform Process Agric"},{"key":"11179_CR21","first-page":"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","journal-title":"J Agric Food Res"},{"issue":"20","key":"11179_CR22","doi-asserted-by":"publisher","first-page":"30709","DOI":"10.1007\/s11042-023-14441-x","volume":"82","author":"M Shantkumari","year":"2023","unstructured":"Shantkumari M, Uma S (2023) Machine learning techniques implementation for detection of grape leaf disease. Multimed Tools Appl 82(20):30709\u201330731","journal-title":"Multimed Tools Appl"},{"issue":"5","key":"11179_CR23","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. Multimedia Tools Appl 81(5):6051\u20136067","journal-title":"Multimedia Tools Appl"},{"key":"11179_CR24","doi-asserted-by":"crossref","unstructured":"Bouacida I, Farou B, Djakhdjakha L, Seridi H, Kurulay M (2024) Innovative deep learning approach for cross-crop plant disease detection: a generalized method for identifying unhealthy leaves. Information Processing in Agriculture","DOI":"10.1016\/j.inpa.2024.03.002"},{"issue":"2","key":"11179_CR25","doi-asserted-by":"publisher","first-page":"327","DOI":"10.3390\/agronomy14020327","volume":"14","author":"U Barman","year":"2024","unstructured":"Barman U, Sarma P, Rahman M, Deka V, Lahkar S, Sharma V, Saikia MJ (2024) Vit-smartagri: vision transformer and smartphone-based plant disease detection for smart agriculture. Agronomy 14(2):327","journal-title":"Agronomy"},{"key":"11179_CR26","doi-asserted-by":"publisher","first-page":"28096","DOI":"10.1109\/ACCESS.2024.3367443","volume":"12","author":"R Maurya","year":"2024","unstructured":"Maurya R, Mahapatra S, Rajput L (2024) A lightweight meta-ensemble approach for plant disease detection suitable for iot-based environments. IEEE Access 12:28096\u201328108","journal-title":"IEEE Access"},{"key":"11179_CR27","unstructured":"Bhujade VG. Soybean leaf dataset for disease classification. https:\/\/www.kaggle.com\/datasets\/vaishaligbhujade\/soybean-leaf-dataset-for-disease-classification. Accessed 15 March 2024"},{"key":"11179_CR28","unstructured":"Arun Pandian J GG. Plant leaf diseases. https:\/\/data.mendeley.com\/datasets\/tywbtsjrjv\/1. Accessed 15 March 2024"},{"key":"11179_CR29","unstructured":"Iranga HA. Leaf disease dataset. https:\/\/www.kaggle.com\/datasets\/asheniranga\/leaf-disease-dataset-combination. Accessed 15 March 2024"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11179-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11179-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11179-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T11:32:45Z","timestamp":1757158365000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11179-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,9]]},"references-count":29,"journal-issue":{"issue":"26","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["11179"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11179-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,9]]},"assertion":[{"value":"30 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 April 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":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}