{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T06:07:32Z","timestamp":1779257252417,"version":"3.51.4"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T00:00:00Z","timestamp":1734912000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T00:00:00Z","timestamp":1734912000000},"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 & Applic"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s00521-024-10830-x","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T06:30:33Z","timestamp":1734935433000},"page":"4531-4544","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Performance evaluation of different deep learning models used for the purpose of healthy and diseased leaves classification of Cherimoya (Annona Cherimola) plant"],"prefix":"10.1007","volume":"37","author":[{"given":"Siddharth Singh","family":"Chouhan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Uday Pratap","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanjeev","family":"Jain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,23]]},"reference":[{"key":"10830_CR1","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.neucom.2021.10.015","volume":"467","author":"VM Ara\u00fajo","year":"2022","unstructured":"Ara\u00fajo VM, Britto AS Jr, Oliveira LS, Koerich AL (2022) Two-view fine-grained classification of plant species. Neurocomputing 467:427\u2013441. https:\/\/doi.org\/10.1016\/j.neucom.2021.10.015","journal-title":"Neurocomputing"},{"key":"10830_CR2","doi-asserted-by":"publisher","first-page":"117470","DOI":"10.1016\/j.eswa.2022.117470","volume":"202","author":"A Beikmohammadi","year":"2022","unstructured":"Beikmohammadi A, Faez K, Motallebi A (2022) SWP-LeafNET: A novel multistage approach for plant leaf identification based on deep CNN. Expert Syst Appl 202:117470. https:\/\/doi.org\/10.1016\/j.eswa.2022.117470","journal-title":"Expert Syst Appl"},{"issue":"3","key":"10830_CR3","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1007\/s11831-018-9257-4","volume":"26","author":"SS Chouhan","year":"2019","unstructured":"Chouhan SS, Kaul A, Singh UP (2019) Image segmentation using computational intelligence techniques: review. Archiv Comput Methods Eng 26(3):533\u2013596. https:\/\/doi.org\/10.1007\/s11831-018-9257-4","journal-title":"Archiv Comput Methods Eng"},{"issue":"2","key":"10830_CR4","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 (2020) Applications of computer vision in plant pathology: a survey. Arch Comput Methods Eng 27(2):611\u2013632. https:\/\/doi.org\/10.1007\/s11831-019-09324-0","journal-title":"Arch Comput Methods Eng"},{"key":"10830_CR5","doi-asserted-by":"publisher","first-page":"108796","DOI":"10.1016\/j.measurement.2020.108796","volume":"171","author":"SS Chouhan","year":"2021","unstructured":"Chouhan SS, Singh UP, Sharma U, Jain S (2021) Leaf disease segmentation and classification of Jatropha Curcas L. and Pongamia Pinnata L. biofuel plants using computer vision based approaches. Measur: J Int Measur Conf 171:108796. https:\/\/doi.org\/10.1016\/j.measurement.2020.108796","journal-title":"Measur: J Int Measur Conf"},{"issue":"4","key":"10830_CR6","doi-asserted-by":"publisher","first-page":"2275","DOI":"10.1007\/s11277-024-11374-y","volume":"136","author":"SS Chouhan","year":"2024","unstructured":"Chouhan SS, Singh UP, Sharma U, Jain S (2024) Classification of different plant species using deep learning and machine learning algorithms. Wireless Pers Commun 136(4):2275\u20132298. https:\/\/doi.org\/10.1007\/s11277-024-11374-y","journal-title":"Wireless Pers Commun"},{"key":"10830_CR7","doi-asserted-by":"publisher","first-page":"100581","DOI":"10.1016\/j.measen.2022.100581","volume":"24","author":"S Dahiya","year":"2022","unstructured":"Dahiya S, Gulati T, Gupta D (2022) Performance analysis of deep learning architectures for plant leaves disease detection. Measur: Sens 24:100581. https:\/\/doi.org\/10.1016\/j.measen.2022.100581","journal-title":"Measur: Sens"},{"key":"10830_CR8","doi-asserted-by":"publisher","first-page":"106892","DOI":"10.1016\/j.compag.2022.106892","volume":"196","author":"X Fan","year":"2022","unstructured":"Fan X, Luo P, Mu Y, Zhou R, Tjahjadi T, Ren Y (2022) Leaf image based plant disease identification using transfer learning and feature fusion. Comput Electron Agric 196:106892. https:\/\/doi.org\/10.1016\/j.compag.2022.106892","journal-title":"Comput Electron Agric"},{"key":"10830_CR9","doi-asserted-by":"publisher","first-page":"100325","DOI":"10.1016\/j.jafr.2022.100325","volume":"9","author":"GC Sunil","year":"2022","unstructured":"Sunil GC, Zhang Y, Koparan C, Ahmed MR, Howatt K, Sun X (2022) Weed and crop species classification using computer vision and deep learning technologies in greenhouse conditions. J Agric Food Res 9:100325. https:\/\/doi.org\/10.1016\/j.jafr.2022.100325","journal-title":"J Agric Food Res"},{"key":"10830_CR10","doi-asserted-by":"publisher","first-page":"101585","DOI":"10.1016\/j.ecoinf.2022.101585","volume":"69","author":"S Ganguly","year":"2022","unstructured":"Ganguly S, Bhowal P, Oliva D, Sarkar R (2022) BLeafNet: a Bonferroni mean operator based fusion of CNN models for plant identification using leaf image classification. Ecol Inform 69:101585. https:\/\/doi.org\/10.1016\/j.ecoinf.2022.101585","journal-title":"Ecol Inform"},{"key":"10830_CR11","doi-asserted-by":"crossref","unstructured":"Guo M-H, Liu Z-N, Mu T-J, Hu S-M (2021) Beyond Self-attention: external attention using two linear layers for visual tasks. https:\/\/arxiv.org\/abs\/2105.02358","DOI":"10.1109\/TPAMI.2022.3211006"},{"issue":"2","key":"10830_CR12","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1016\/S2095-3119(21)63604-3","volume":"21","author":"Y Guo-feng","year":"2022","unstructured":"Guo-feng Y, Yong Y, Zi-kang HE, Xin-yu Z, Yong HE (2022) A rapid, low-cost deep learning system to classify strawberry disease based on cloud service. J Integr Agric 21(2):460\u2013473. https:\/\/doi.org\/10.1016\/S2095-3119(21)63604-3","journal-title":"J Integr Agric"},{"issue":"10","key":"10830_CR13","doi-asserted-by":"publisher","first-page":"7407","DOI":"10.1007\/S00521-022-08003-9\/METRICS","volume":"35","author":"MA Haque","year":"2023","unstructured":"Haque MA, Marwaha S, Deb CK, Nigam S, Arora A (2023) Recognition of diseases of maize crop using deep learning models. Neural Comput Appl 35(10):7407\u20137421. https:\/\/doi.org\/10.1007\/S00521-022-08003-9\/METRICS","journal-title":"Neural Comput Appl"},{"issue":"20","key":"10830_CR14","doi-asserted-by":"publisher","first-page":"14855","DOI":"10.1007\/s00521-023-08496-y","volume":"35","author":"P Hari","year":"2023","unstructured":"Hari P, Singh MP (2023) A lightweight convolutional neural network for disease detection of fruit leaves. Neural Comput Appl 35(20):14855\u201314866. https:\/\/doi.org\/10.1007\/s00521-023-08496-y","journal-title":"Neural Comput Appl"},{"key":"10830_CR15","doi-asserted-by":"publisher","first-page":"101197","DOI":"10.1016\/j.ecoinf.2020.101197","volume":"61","author":"RC Joshi","year":"2021","unstructured":"Joshi RC, Kaushik M, Dutta MK, Srivastava A, Choudhary N (2021) VirLeafNet: automatic analysis and viral disease diagnosis using deep-learning in Vigna mungo plant. Ecol Inform 61:101197. https:\/\/doi.org\/10.1016\/j.ecoinf.2020.101197","journal-title":"Ecol Inform"},{"issue":"20","key":"10830_CR16","doi-asserted-by":"publisher","first-page":"11919","DOI":"10.1007\/s00521-024-09670-6","volume":"36","author":"S Karande","year":"2024","unstructured":"Karande S, Garg B (2024) Performance evaluation and optimization of convolutional neural network architectures for Tomato plant disease eleven classes based on augmented leaf images dataset. Neural Comput Appl 36(20):11919\u201311943. https:\/\/doi.org\/10.1007\/s00521-024-09670-6","journal-title":"Neural Comput Appl"},{"key":"10830_CR17","doi-asserted-by":"publisher","first-page":"101679","DOI":"10.1016\/j.ecoinf.2022.101679","volume":"69","author":"AS Keceli","year":"2022","unstructured":"Keceli AS, Kaya A, Catal C, Tekinerdogan B (2022) Deep learning-based multi-task prediction system for plant disease and species detection. Ecol Inform 69:101679. https:\/\/doi.org\/10.1016\/j.ecoinf.2022.101679","journal-title":"Ecol Inform"},{"key":"10830_CR18","doi-asserted-by":"publisher","first-page":"110425","DOI":"10.1016\/j.measurement.2021.110425","volume":"188","author":"M Koklu","year":"2022","unstructured":"Koklu M, Unlersen MF, Ozkan IA, Aslan MF, Sabanci K (2022) A CNN-SVM study based on selected deep features for grapevine leaves classification. Measurement 188:110425. https:\/\/doi.org\/10.1016\/j.measurement.2021.110425","journal-title":"Measurement"},{"key":"10830_CR19","doi-asserted-by":"crossref","unstructured":"Kolesnikov A, Beyer L, Zhai X, Puigcerver J, Yung J, Gelly S, Houlsby N (2020) Big Transfer (BiT): General Visual Representation Learning. https:\/\/arxiv.org\/abs\/1912.11370","DOI":"10.1007\/978-3-030-58558-7_29"},{"key":"10830_CR20","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.aiia.2022.11.002","volume":"6","author":"N Kundu","year":"2022","unstructured":"Kundu N, Rani G, Dhaka VS, Gupta K, Nayaka SC, Vocaturo E, Zumpano E (2022) Disease detection, severity prediction, and crop loss estimation in MaizeCrop using deep learning. Artif Intell Agric 6:276\u2013291. https:\/\/doi.org\/10.1016\/j.aiia.2022.11.002","journal-title":"Artif Intell Agric"},{"key":"10830_CR21","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. https:\/\/arxiv.org\/abs\/2103.14030","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10830_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-024-10409-6","author":"MHK Mehedi","year":"2024","unstructured":"Mehedi MHK, Nawer N, Ahmed S, Khan MSI, Hasib KM, Mridha MF, Alam MdGR, Nguyen TT (2024) PLD-Det: plant leaf disease detection in real time using an end-to-end neural network approach based on improved YOLOv7. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-024-10409-6","journal-title":"Neural Comput Appl"},{"key":"10830_CR23","doi-asserted-by":"publisher","first-page":"101663","DOI":"10.1016\/j.ecoinf.2022.101663","volume":"69","author":"BN Naik","year":"2022","unstructured":"Naik BN, Malmathanraj R, Palanisamy P (2022) Detection and classification of chilli leaf disease using a squeeze-and-excitation-based CNN model. Ecol Inform 69:101663. https:\/\/doi.org\/10.1016\/j.ecoinf.2022.101663","journal-title":"Ecol Inform"},{"key":"10830_CR24","doi-asserted-by":"publisher","first-page":"106915","DOI":"10.1016\/j.compag.2022.106915","volume":"197","author":"M Nandhini","year":"2022","unstructured":"Nandhini M, Kala KU, Thangadarshini M, Madhusudhana Verma S (2022) Deep Learning model of sequential image classifier for crop disease detection in plantain tree cultivation. Comput Electron Agric 197:106915. https:\/\/doi.org\/10.1016\/j.compag.2022.106915","journal-title":"Comput Electron Agric"},{"key":"10830_CR25","doi-asserted-by":"publisher","unstructured":"Panchal B, Pranjal P, Patel RK, Sharma A, Chouhan SS (2024) Assessing the quantity of a crop field using aerial images. In Chouhan SS, Singh UP, Jain S (Eds), Applications of Computer Vision and Drone Technology in Agriculture 4.0 (pp 187\u2013198). Springer Nature Singapore. https:\/\/doi.org\/10.1007\/978-981-99-8684-2_11","DOI":"10.1007\/978-981-99-8684-2_11"},{"key":"10830_CR26","doi-asserted-by":"publisher","first-page":"101725","DOI":"10.1016\/j.ecoinf.2022.101725","volume":"70","author":"A Pandey","year":"2022","unstructured":"Pandey A, Jain K (2022) A robust deep attention dense convolutional neural network for plant leaf disease identification and classification from smart phone captured real world images. Ecol Inform 70:101725. https:\/\/doi.org\/10.1016\/j.ecoinf.2022.101725","journal-title":"Ecol Inform"},{"key":"10830_CR27","doi-asserted-by":"publisher","first-page":"109795","DOI":"10.1016\/j.compeleceng.2024.109795","volume":"120","author":"RK Patel","year":"2024","unstructured":"Patel RK, Choudhary A, Chouhan SS, Pandey KK (2024) Mango leaf disease diagnosis using Total Variation Filter Based Variational Mode Decomposition. Comput Electr Eng 120:109795. https:\/\/doi.org\/10.1016\/j.compeleceng.2024.109795","journal-title":"Comput Electr Eng"},{"key":"10830_CR28","doi-asserted-by":"publisher","first-page":"108357","DOI":"10.1016\/j.compeleceng.2022.108357","volume":"103","author":"N Raj","year":"2022","unstructured":"Raj N, Perumal S, Singla S, Sharma GK, Qamar S, Chakkaravarthy AP (2022) Computer aided agriculture development for crop disease detection by segmentation and classification using deep learning architectures. Comput Electr Eng 103:108357. https:\/\/doi.org\/10.1016\/j.compeleceng.2022.108357","journal-title":"Comput Electr Eng"},{"key":"10830_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.108492","volume":"105","author":"SRG Reddy","year":"2023","unstructured":"Reddy SRG, Varma GPS, Davuluri RL (2023) Resnet-based modified red deer optimization with DLCNN classifier for plant disease identification and classification. Comput Electr Eng 105:108492. https:\/\/doi.org\/10.1016\/j.compeleceng.2022.108492","journal-title":"Comput Electr Eng"},{"key":"10830_CR30","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2019) MobileNetV2: inverted residuals and linear bottlenecks. https:\/\/arxiv.org\/abs\/1801.04381","DOI":"10.1109\/CVPR.2018.00474"},{"issue":"23","key":"10830_CR31","doi-asserted-by":"publisher","first-page":"14395","DOI":"10.1007\/s00521-024-09812-w","volume":"36","author":"E Saraswathi","year":"2024","unstructured":"Saraswathi E, Banu JF (2024) Hybrid CGAN-based plant leaf disease classification using OTSU and surf feature extraction. Neural Comput Appl 36(23):14395\u201314407. https:\/\/doi.org\/10.1007\/s00521-024-09812-w","journal-title":"Neural Comput Appl"},{"key":"10830_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/s41870-020-00477-x","author":"UP Singh","year":"2020","unstructured":"Singh UP, Chouhan SS, Jain S (2020) Images as graphical password: verification and analysis using non-regular low-density parity check coding. Int J Inf Technol. https:\/\/doi.org\/10.1007\/s41870-020-00477-x","journal-title":"Int J Inf Technol"},{"issue":"9","key":"10830_CR33","doi-asserted-by":"publisher","first-page":"6737","DOI":"10.1007\/s00521-022-07793-2","volume":"35","author":"A Stephen","year":"2023","unstructured":"Stephen A, Punitha A, Chandrasekar A (2023) Designing self attention-based ResNet architecture for rice leaf disease classification. Neural Comput Appl 35(9):6737\u20136751. https:\/\/doi.org\/10.1007\/s00521-022-07793-2","journal-title":"Neural Comput Appl"},{"key":"10830_CR34","unstructured":"Tan M, Le QV (2020) EfficientNet: rethinking model scaling for convolutional neural networks. https:\/\/arxiv.org\/abs\/1905.11946"},{"key":"10830_CR35","doi-asserted-by":"publisher","first-page":"100568","DOI":"10.1016\/j.measen.2022.100568","volume":"24","author":"A Umamageswari","year":"2022","unstructured":"Umamageswari A, Deepa S, Raja K (2022) An enhanced approach for leaf disease identification and classification using deep learning techniques. Measur: Sens 24:100568. https:\/\/doi.org\/10.1016\/j.measen.2022.100568","journal-title":"Measur: Sens"},{"key":"10830_CR36","doi-asserted-by":"publisher","first-page":"101940","DOI":"10.1016\/j.pmpp.2022.101940","volume":"123","author":"L Xu","year":"2023","unstructured":"Xu L, Cao B, Zhao F, Ning S, Xu P, Zhang W, Hou X (2023) Wheat leaf disease identification based on deep learning algorithms. Physiol Mol Plant Pathol 123:101940. https:\/\/doi.org\/10.1016\/j.pmpp.2022.101940","journal-title":"Physiol Mol Plant Pathol"},{"key":"10830_CR37","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.biosystemseng.2022.09.006","volume":"223","author":"X Zhang","year":"2022","unstructured":"Zhang X, Xun Y, Chen Y (2022) Automated identification of citrus diseases in orchards using deep learning. Biosyst Eng 223:249\u2013258. https:\/\/doi.org\/10.1016\/j.biosystemseng.2022.09.006","journal-title":"Biosyst Eng"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10830-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10830-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10830-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,8]],"date-time":"2025-02-08T19:34:43Z","timestamp":1739043283000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10830-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,23]]},"references-count":37,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["10830"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10830-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,23]]},"assertion":[{"value":"25 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"On behalf of all the authors the corresponding authors declare that there is no conflict of interest to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}