{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T05:24:32Z","timestamp":1773725072606,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"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":[[2026,2]]},"DOI":"10.1007\/s00521-025-11726-0","type":"journal-article","created":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T05:14:16Z","timestamp":1771650856000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A multi-scale deep learning approach for nutrient deficiency detection in banana plants using adaptive feature pooling"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1217-891X","authenticated-orcid":false,"given":"C. K.","family":"Sunil","sequence":"first","affiliation":[]},{"given":"R.","family":"Anusha","sequence":"additional","affiliation":[]},{"given":"C. K.","family":"Raghavendra","sequence":"additional","affiliation":[]},{"given":"Guowei","family":"Dai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,21]]},"reference":[{"issue":"5","key":"11726_CR1","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1002\/efd2.110","volume":"4","author":"P Kumari","year":"2023","unstructured":"Kumari P, Gaur SS, Tiwari RK (2023) Banana and its by-products: a comprehensive review on its nutritional composition and pharmacological benefits. eFood 4(5):110","journal-title":"eFood"},{"key":"11726_CR2","doi-asserted-by":"crossref","unstructured":"Fan C-L, Chung Y-J, Yen S-M (2025) Comparative analysis of deep learning models for banana plant detection in uav rgb and grayscale imagery. Comput Mat Contin 84(3)","DOI":"10.32604\/cmc.2025.066856"},{"key":"11726_CR3","doi-asserted-by":"publisher","first-page":"87333","DOI":"10.1109\/ACCESS.2022.3199926","volume":"10","author":"P Sahu","year":"2022","unstructured":"Sahu P, Singh AP, Chug A, Singh D (2022) A systematic literature review of machine learning techniques deployed in agriculture: A case study of banana crop. IEEE Access 10:87333\u201387360","journal-title":"IEEE Access"},{"key":"11726_CR4","doi-asserted-by":"crossref","unstructured":"Guerrero R, Renteros B, Castaneda R, Villanueva A, Belup\u00fa I (2021) Detection of nutrient deficiencies in banana plants using deep learning. In: 2021 IEEE international conference on automation\/XXIV congress of the Chilean association of automatic control (ICA-ACCA), pp 1\u20137. IEEE","DOI":"10.1109\/ICAACCA51523.2021.9465311"},{"key":"11726_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2022.107911","volume":"41","author":"J Nakatumba-Nabende","year":"2022","unstructured":"Nakatumba-Nabende J, Tusubira JF, Babirye C, Nsumba S, Abu CO (2022) A dataset of cassava whitefly count images. Data Brief 41:107911","journal-title":"Data Brief"},{"key":"11726_CR6","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1016\/j.compag.2019.04.035","volume":"162","author":"JGA Barbedo","year":"2019","unstructured":"Barbedo JGA (2019) Detection of nutrition deficiencies in plants using proximal images and machine learning: a review. Comput Electron Agric 162:482\u2013492","journal-title":"Comput Electron Agric"},{"key":"11726_CR7","doi-asserted-by":"crossref","unstructured":"Graeff S, Pfenning J, Claupein W, Liebig H-P (2008) Evaluation of image analysis to determine the n-fertilizer demand of broccoli plants (brassica oleracea convar. botrytis var. italica). Adv Opt Technol","DOI":"10.1155\/2008\/359760"},{"key":"11726_CR8","doi-asserted-by":"crossref","unstructured":"Manihira NR, Sethy PK (2025) Transfer learning-based lightweight mobilenetv2 with cbam attention for rice leaf nutrient deficiency detection. Neural Comput Appl 1\u201320","DOI":"10.1007\/s00521-025-11665-w"},{"issue":"4","key":"11726_CR9","first-page":"385","volume":"8","author":"C Sunil","year":"2022","unstructured":"Sunil C, Jaidhar C, Patil N (2022) Binary class and multi-class plant disease detection using ensemble deep learning-based approach. Int J Sustain Agric Manag Inform 8(4):385\u2013407","journal-title":"Int J Sustain Agric Manag Inform"},{"issue":"12","key":"11726_CR10","doi-asserted-by":"publisher","first-page":"14955","DOI":"10.1007\/s10462-023-10517-0","volume":"56","author":"C Sunil","year":"2023","unstructured":"Sunil C, Jaidhar C, Patil N (2023) Systematic study on deep learning-based plant disease detection or classification. Artif Intell Rev 56(12):14955\u201315052","journal-title":"Artif Intell Rev"},{"key":"11726_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106100","volume":"184","author":"Q Shicheng","year":"2021","unstructured":"Shicheng Q, Youwen T, Qinghu W, Shiyuan S, Ping S (2021) Nondestructive detection of decayed blueberry based on information fusion of hyperspectral imaging (HSI) and low-field nuclear magnetic resonance (lf-nmr). Comput Electron Agric 184:106100","journal-title":"Comput Electron Agric"},{"key":"11726_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105288","volume":"170","author":"AY Khaled","year":"2020","unstructured":"Khaled AY, Abd Aziz S, Bejo SK, Nawi NM, Jamaludin D, Ibrahim NUA (2020) A comparative study on dimensionality reduction of dielectric spectral data for the classification of basal stem rot (bsr) disease in oil palm. Comput Electron Agric 170:105288","journal-title":"Comput Electron Agric"},{"issue":"2","key":"11726_CR13","doi-asserted-by":"publisher","first-page":"1258","DOI":"10.1109\/TASE.2020.3041499","volume":"19","author":"QH Cap","year":"2020","unstructured":"Cap QH, Uga H, Kagiwada S, Iyatomi H (2020) Leafgan: an effective data augmentation method for practical plant disease diagnosis. IEEE Trans Autom Sci Eng 19(2):1258\u20131267","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"11726_CR14","doi-asserted-by":"crossref","unstructured":"Stasenko N, Shadrin D, Katrutsa A, Somov A (2023) Dynamic mode decomposition and deep learning for postharvest decay prediction in apples. IEEE Trans Instrum Meas","DOI":"10.1109\/TIM.2023.3284918"},{"key":"11726_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2021.3111994","volume":"70","author":"S Azimi","year":"2021","unstructured":"Azimi S, Wadhawan R, Gandhi TK (2021) Intelligent monitoring of stress induced by water deficiency in plants using deep learning. IEEE Trans Instrum Meas 70:1\u201313","journal-title":"IEEE Trans Instrum Meas"},{"issue":"11","key":"11726_CR16","doi-asserted-by":"publisher","first-page":"9010","DOI":"10.1109\/TIM.2020.2997064","volume":"69","author":"S Hazarika","year":"2020","unstructured":"Hazarika S, Choudhury R, Montazer B, Medhi S, Goswami MP, Sarma U (2020) Detection of citrus tristeza virus in mandarin orange using a custom-developed electronic nose system. IEEE Trans Instrum Meas 69(11):9010\u20139018","journal-title":"IEEE Trans Instrum Meas"},{"key":"11726_CR17","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1016\/j.compag.2019.01.030","volume":"157","author":"H Tavakoli","year":"2019","unstructured":"Tavakoli H, Gebbers R (2019) Assessing nitrogen and water status of winter wheat using a digital camera. Comput Electron Agric 157:558\u2013567","journal-title":"Comput Electron Agric"},{"key":"11726_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.atech.2023.100214","volume":"4","author":"MAB Bhuiyan","year":"2023","unstructured":"Bhuiyan MAB, Abdullah HM, Arman SE, Rahman SS, Al Mahmud K (2023) Bananasqueezenet: a very fast, lightweight convolutional neural network for the diagnosis of three prominent banana leaf diseases. Smart Agric Technol 4:100214","journal-title":"Smart Agric Technol"},{"key":"11726_CR19","doi-asserted-by":"publisher","first-page":"1783","DOI":"10.1016\/j.matpr.2021.10.466","volume":"56","author":"R Sathyavani","year":"2022","unstructured":"Sathyavani R, JaganMohan K, Kalaavathi B (2022) Classification of nutrient deficiencies in rice crop using densenet-bc. Mater Today Proc 56:1783\u20131789","journal-title":"Mater Today Proc"},{"key":"11726_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmpp.2023.102158","volume":"128","author":"EG El-Dawy","year":"2023","unstructured":"El-Dawy EG, Hussein MA, Yassein AS (2023) Virulence, inulinolytic potency and biological control of fungi associated with banana leaf blight disease. Physiol Mol Plant Pathol 128:102158","journal-title":"Physiol Mol Plant Pathol"},{"issue":"6","key":"11726_CR21","doi-asserted-by":"publisher","first-page":"2522","DOI":"10.1007\/s11119-023-10051-7","volume":"24","author":"G Sun","year":"2023","unstructured":"Sun G, Hu T, Chen S, Sun J, Zhang J, Ye R, Zhang S, Liu J (2023) Using UAV-based multispectral remote sensing imagery combined with DRIS method to diagnose leaf nitrogen nutrition status in a fertigated apple orchard. Precision Agric 24(6):2522\u20132548","journal-title":"Precision Agric"},{"issue":"1","key":"11726_CR22","doi-asserted-by":"publisher","first-page":"14939","DOI":"10.1038\/s41598-023-42190-2","volume":"13","author":"H You","year":"2023","unstructured":"You H, Zhou M, Zhang J, Peng W, Sun C (2023) Sugarcane nitrogen nutrition estimation with digital images and machine learning methods. Sci Rep 13(1):14939","journal-title":"Sci Rep"},{"key":"11726_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.biosystemseng.2021.08.035","volume":"211","author":"M Noguera","year":"2021","unstructured":"Noguera M, Aquino A, Ponce JM, Cordeiro A, Silvestre J, Arias-Calder\u00f3n R, Encarna\u00e7\u00e3o Marcelo M, Jord\u00e3o P, And\u00fajar JM (2021) Nutritional status assessment of olive crops by means of the analysis and modelling of multispectral images taken with uavs. Biosys Eng 211:1\u201318","journal-title":"Biosys Eng"},{"issue":"1","key":"11726_CR24","doi-asserted-by":"publisher","first-page":"14108","DOI":"10.1038\/s41598-023-41120-6","volume":"13","author":"FD Shadrach","year":"2023","unstructured":"Shadrach FD, Kandasamy G, Neelakandan S, Lingaiah TB (2023) Optimal transfer learning based nutrient deficiency classification model in ridge gourd (luffa acutangula). Sci Rep 13(1):14108","journal-title":"Sci Rep"},{"key":"11726_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.atech.2022.100155","volume":"4","author":"MSH Talukder","year":"2023","unstructured":"Talukder MSH, Sarkar AK (2023) Nutrients deficiency diagnosis of rice crop by weighted average ensemble learning. Smart Agric Technol 4:100155","journal-title":"Smart Agric Technol"},{"key":"11726_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.atech.2023.100204","volume":"4","author":"O Hassanijalilian","year":"2023","unstructured":"Hassanijalilian O, Igathinathane C, Day S, Bajwa S, Nowatzki J (2023) Measuring soybean iron deficiency chlorosis progression and yield prediction with unmanned aerial vehicle. Smart Agric Technol 4:100204","journal-title":"Smart Agric Technol"},{"issue":"6","key":"11726_CR27","doi-asserted-by":"publisher","first-page":"2327","DOI":"10.1007\/s11119-023-10042-8","volume":"24","author":"S Xu","year":"2023","unstructured":"Xu S, Xu X, Zhu Q, Meng Y, Yang G, Feng H, Yang M, Zhu Q, Xue H, Wang B (2023) Monitoring leaf nitrogen content in rice based on information fusion of multi-sensor imagery from UAV. Precis Agric 24(6):2327\u20132349","journal-title":"Precis Agric"},{"key":"11726_CR28","doi-asserted-by":"crossref","unstructured":"Rawal A, Hartemink A, Zhang Y, Wang Y, Lankau RA, Ruark MD (2023) Visible and near-infrared spectroscopy predicted leaf nitrogen contents of potato varieties under different growth and management conditions. Precis Agric 1\u201320","DOI":"10.1007\/s11119-023-10091-z"},{"issue":"4","key":"11726_CR29","first-page":"581","volume":"8","author":"D Mathew","year":"2021","unstructured":"Mathew D, Kumar CS, Cherian KA (2021) Foliar fungal disease classification in banana plants using elliptical local binary pattern on multiresolution dual tree complex wavelet transform domain. Info Proc Agric 8(4):581\u2013592","journal-title":"Info Proc Agric"},{"key":"11726_CR30","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1850\/1\/012050","volume":"1850","author":"A Jose","year":"2021","unstructured":"Jose A, Nandagopalan S, Ubalanka V, Viswanath D (2021) Detection and classification of nutrient deficiencies in plants using machine learning. J Phys Conf Ser 1850:012050","journal-title":"J Phys Conf Ser"},{"issue":"3","key":"11726_CR31","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1016\/j.eswa.2012.05.042","volume":"40","author":"C Elmas","year":"2013","unstructured":"Elmas C, Demirci R, G\u00fcvenc U (2013) Fuzzy diffusion filter with extended neighborhood. Expert Syst Appl 40(3):866\u2013872","journal-title":"Expert Syst Appl"},{"key":"11726_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2022.103320","volume":"175","author":"M Janani","year":"2023","unstructured":"Janani M, Jebakumar R (2023) Detection and classification of groundnut leaf nutrient level extraction in RGB images. Adv Eng Softw 175:103320","journal-title":"Adv Eng Softw"},{"issue":"5","key":"11726_CR33","doi-asserted-by":"publisher","first-page":"4553","DOI":"10.1007\/s11760-024-03094-4","volume":"18","author":"K Venkatesh","year":"2024","unstructured":"Venkatesh K, Naik KJ (2024) Nutrient deficiency identification and yield-loss prediction in leaf images of groundnut crop using transfer learning. SIViP 18(5):4553\u20134568","journal-title":"SIViP"},{"issue":"1","key":"11726_CR34","doi-asserted-by":"publisher","first-page":"15537","DOI":"10.1038\/s41598-024-66543-7","volume":"14","author":"A Bera","year":"2024","unstructured":"Bera A, Bhattacharjee D, Krejcar O (2024) PND-Net: plant nutrition deficiency and disease classification using graph convolutional network. Sci Rep 14(1):15537","journal-title":"Sci Rep"},{"issue":"1","key":"11726_CR35","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/s42044-024-00209-5","volume":"8","author":"S Nikitha","year":"2025","unstructured":"Nikitha S, Prabhanjan S, Sathyanarayan A (2025) Plant nutritional deficiency detection: a survey of predictive analytics approaches. Iran J Comput Sci 8(1):83\u2013101","journal-title":"Iran J Comput Sci"},{"issue":"10","key":"11726_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-025-11234-6","volume":"58","author":"TD Salka","year":"2025","unstructured":"Salka TD, Hanafi MB, Rahman SM, Zulperi DBM, Omar Z (2025) Plant leaf disease detection and classification using convolution neural networks model: a review. Artif Intell Rev 58(10):1\u201366","journal-title":"Artif Intell Rev"},{"key":"11726_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2024.109720","volume":"229","author":"P Hari","year":"2025","unstructured":"Hari P, Singh MP (2025) Adaptive knowledge transfer using federated deep learning for plant disease detection. Comput Electron Agric 229:109720","journal-title":"Comput Electron Agric"},{"key":"11726_CR38","doi-asserted-by":"crossref","unstructured":"Panchananam SL, Chandaliya PK, Akhtar Z, Upla K, Ramachandra R (2025) Waveletfusion: enhancing plant leaf disease classification with multi-scale feature extraction and explainable AI. Expert Syst Appl 127947","DOI":"10.1016\/j.eswa.2025.127947"},{"key":"11726_CR39","doi-asserted-by":"crossref","unstructured":"Srivastava M, Sisaudia V, Meena J (2025) AGRITL-VIT: A vision transformer model with attention techniques for classification of plant leaf disease. Expert Syst Appl 128793","DOI":"10.1016\/j.eswa.2025.128793"},{"key":"11726_CR40","doi-asserted-by":"crossref","unstructured":"Kumar T, Kaur S, Sharma P, Chhikara A, Cheng X, Lalar S, Verma V (2025) Plant disease detection and classification using hybrid model based on convolutional auto encoder and convolutional neural network. Comput Mat Contin 83(3)","DOI":"10.32604\/cmc.2025.062010"},{"key":"11726_CR41","doi-asserted-by":"crossref","unstructured":"Das A, Mahto R, Wang W, Jamali A, Singh P, Roy SK (2025) A generative framework for detection and classification of plant leaf disease using diffusion network. Appl Soft Comput 113152","DOI":"10.1016\/j.asoc.2025.113152"},{"key":"11726_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120381","volume":"228","author":"C Sunil","year":"2023","unstructured":"Sunil C, Jaidhar C, Patil N (2023) Tomato plant disease classification using multilevel feature fusion with adaptive channel spatial and pixel attention mechanism. Expert Syst Appl 228:120381","journal-title":"Expert Syst Appl"},{"key":"11726_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2023.108481","volume":"216","author":"G Dai","year":"2024","unstructured":"Dai G, Tian Z, Fan J, Sunil C, Dewi C (2024) DFN-PSAN: multi-level deep information feature fusion extraction network for interpretable plant disease classification. Comput Electron Agric 216:108481","journal-title":"Comput Electron Agric"},{"key":"11726_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2023.108129","volume":"212","author":"G Dai","year":"2023","unstructured":"Dai G, Fan J, Dewi C (2023) ITF-WPI: image and text based cross-modal feature fusion model for wolfberry pest recognition. Comput Electron Agric 212:108129","journal-title":"Comput Electron Agric"},{"key":"11726_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2023.109155","volume":"48","author":"P Sunitha","year":"2023","unstructured":"Sunitha P, Uma B, Channakeshava S, Babu S (2023) A fully labelled image dataset of banana leaves deficient in nutrients. Data Brief 48:109155","journal-title":"Data Brief"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11726-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11726-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11726-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T03:43:34Z","timestamp":1773719014000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11726-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":45,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["11726"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11726-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"5 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2026","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 report no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human or animal subjects.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}}],"article-number":"71"}}