{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T13:21:07Z","timestamp":1780579267942,"version":"3.54.1"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T00:00:00Z","timestamp":1740009600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T00:00:00Z","timestamp":1740009600000},"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":[[2025,5]]},"DOI":"10.1007\/s00521-025-11012-z","type":"journal-article","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T11:42:07Z","timestamp":1740051727000},"page":"8309-8329","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Spatial attention-based hybrid VGG-SVM and VGG-RF frameworks for improved cotton leaf disease detection"],"prefix":"10.1007","volume":"37","author":[{"given":"V.","family":"Pandiyaraju","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B.","family":"Anusha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A. M.","family":"Senthil Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K.","family":"Jaspin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shravan","family":"Venkatraman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.","family":"Kannan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,20]]},"reference":[{"issue":"2","key":"11012_CR1","first-page":"451","volume":"54","author":"V Pandiyaraju","year":"2017","unstructured":"Pandiyaraju V, Perumal PS, Kannan A, Ramesh LS (2017) Smart terrace gardening with intelligent roof control algorithm for water conservation. Pak J Agri Sci 54(2):451\u2013455","journal-title":"Pak J Agri Sci"},{"key":"11012_CR2","doi-asserted-by":"crossref","unstructured":"Santhosh, D., V. Pandiyaraju, and A. Kannan. \"Non-naive bayesian classifier for farmer advisory system.\" In 2014 Sixth international conference on advanced computing (ICoAC), pp. 248\u2013254. IEEE, 2014.","DOI":"10.1109\/ICoAC.2014.7229720"},{"key":"11012_CR3","doi-asserted-by":"crossref","first-page":"107164","DOI":"10.1016\/j.asoc.2021.107164","volume":"103","author":"F Saeed","year":"2021","unstructured":"Saeed F, Khan MA, Sharif M, Mittal M, Goyal LM, 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","journal-title":"Appl Soft Comput"},{"key":"11012_CR4","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.compag.2018.07.032","volume":"153","author":"Z Iqbal","year":"2018","unstructured":"Iqbal Z, Khan MA, Sharif M, Shah JH, Ur Rehman MH, Javed K (2018) An automated detection and classification of citrus plant diseases using image processing techniques: a review. Comput Electron Agric 153:12\u201332","journal-title":"Comput Electron Agric"},{"issue":"9","key":"11012_CR5","doi-asserted-by":"crossref","first-page":"3169","DOI":"10.3390\/s21093169","volume":"21","author":"RF Caldeira","year":"2021","unstructured":"Caldeira RF, Santiago WE, Teruel B (2021) Identification of cotton leaf lesions using deep learning techniques. Sensors 21(9):3169","journal-title":"Sensors"},{"key":"11012_CR6","doi-asserted-by":"crossref","unstructured":"Prashar, K., Talwar, R., & Kant, C. (2019, April). CNN based on overlapping pooling method and multi-layered learning with SVM & KNN for American cotton leaf disease recognition. In 2019 international conference on automation, computational and technology management (ICACTM) (pp. 330\u2013333). IEEE.","DOI":"10.1109\/ICACTM.2019.8776730"},{"key":"11012_CR7","doi-asserted-by":"crossref","unstructured":"Prajapati B.S, Dabhi V.K and Prajapati H.B, \"A survey on detection and classification of cotton leaf diseases,\" In 2016 international conference on electrical, electronics, and optimization techniques (ICEEOT), Chennai, India, 2016, pp. 2499\u20132506.","DOI":"10.1109\/ICEEOT.2016.7755143"},{"key":"11012_CR8","doi-asserted-by":"crossref","unstructured":"Zhao, J., Liu, M., & Yao, M. (2009, November). Study on image recognition of insect pest of sugarcane cotton aphis based on rough set and fuzzy C-means clustering. In 2009 third international symposium on intelligent information technology application (Vol. 2, pp. 553\u2013555). IEEE.","DOI":"10.1109\/IITA.2009.295"},{"key":"11012_CR9","doi-asserted-by":"crossref","unstructured":"Senthilnayaki, B., Venkatalakshmi, K. and Kannan, A., 2015, March. Intrusion detection using optimal genetic feature selection and SVM based classifier. In 2015 3rd international conference on signal processing, communication and networking (ICSCN) pp. 1\u20134, IEEE, 2015.","DOI":"10.1109\/ICSCN.2015.7219890"},{"key":"11012_CR10","doi-asserted-by":"crossref","unstructured":"Rothe, P. R., & Kshirsagar, R. V. (2014, November). Adaptive neuro-fuzzy inference system for recognition of cotton leaf diseases. In 2014 innovative applications of computational intelligence on power, energy and controls with their impact on humanity (CIPECH) (pp. 12\u201317). IEEE.","DOI":"10.1109\/CIPECH.2014.7019039"},{"issue":"10","key":"11012_CR11","doi-asserted-by":"crossref","first-page":"101803","DOI":"10.1016\/j.jksuci.2023.101803","volume":"35","author":"V Pandiyaraju","year":"2023","unstructured":"Pandiyaraju V, Ganapathy S, Mohith N, Kannan A (2023) An optimal energy utilization model for precision agriculture in WSNs using multi-objective clustering and deep learning. J King Saud Univ-Comput Inf Sci 35(10):101803","journal-title":"J King Saud Univ-Comput Inf Sci"},{"issue":"12","key":"11012_CR12","doi-asserted-by":"crossref","first-page":"2029","DOI":"10.3390\/agriculture12122029","volume":"12","author":"J Amin","year":"2022","unstructured":"Amin J, Anjum MA, Sharif M, Kadry S, Kim J (2022) Explainable neural network for classification of cotton leaf diseases. Agriculture 12(12):2029","journal-title":"Agriculture"},{"key":"11012_CR13","doi-asserted-by":"crossref","unstructured":"Harshitha G, Kumar S, Rani S and Jain A, \"Cotton disease detection based on deep learning techniques,\" In: 4th smart cities symposium (SCS 2021), online conference, Bahrain, 2021, pp. 496-501","DOI":"10.1049\/icp.2022.0393"},{"key":"11012_CR14","first-page":"200278","volume":"20","author":"MM Islam","year":"2023","unstructured":"Islam MM, Talukder MA, Sarker MRA, Uddin MA, Akhter A, Sharmin S, Al Mamun MS, Debnath SK (2023) A deep learning model for cotton disease prediction using fine-tuning with smart web application in agriculture. Intell Syst Appl 20:200278","journal-title":"Intell Syst Appl"},{"issue":"6","key":"11012_CR15","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1109\/MSP.2012.2211477","volume":"29","author":"Li Deng","year":"2012","unstructured":"Deng Li (2012) The MNIST database of handwritten digit images for machine learning research [Best of the Web]. IEEE Signal Process Mag 29(6):141\u2013142. https:\/\/doi.org\/10.1109\/MSP.2012.2211477","journal-title":"IEEE Signal Process Mag"},{"key":"11012_CR16","unstructured":"Kaggle. Cotton Disease Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/janmejaybhoi\/cotton-disease-dataset."},{"issue":"1","key":"11012_CR17","doi-asserted-by":"crossref","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 Prot 128(1):73\u201386","journal-title":"J Plant Dis Prot"},{"issue":"2","key":"11012_CR18","doi-asserted-by":"crossref","first-page":"380","DOI":"10.31127\/tuje.1406755","volume":"8","author":"U Hyder","year":"2024","unstructured":"Hyder U, Talpur MRH (2024) Detection of cotton leaf disease with machine learning model. Turkish J Eng 8(2):380\u2013393","journal-title":"Turkish J Eng"},{"issue":"3","key":"11012_CR19","first-page":"1942","volume":"33","author":"H Kukadiya","year":"2024","unstructured":"Kukadiya H, Arora N, Meva D, Srivastava S (2024) An ensemble deep learning model for automatic classification of cotton leaves diseases. Ind J Electr Eng Comput Sci 33(3):1942\u20131949","journal-title":"Ind J Electr Eng Comput Sci"},{"issue":"100443","key":"11012_CR20","first-page":"1","volume":"28","author":"SK Noon","year":"2020","unstructured":"Noon SK, Amjad M, Qureshi MA, Mannan A (2020) Use of deep learning techniques for identification of plant leaf stresses: a review. Sustain Comput Inf Syst 28(100443):1\u201315","journal-title":"Sustain Comput Inf Syst"},{"key":"11012_CR21","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1002\/9781119792611.ch7","volume-title":"Machine learning for healthcare applications","author":"SP Perumal","year":"2021","unstructured":"Perumal SP, Ganapathy Sannasi M, Selvi KA (2021) Semantic framework in healthcare. In: Mohanty Sachi Nandan, Nalinipriya G, Jena Om Prakash, Sarkar Achyuth (eds) Machine learning for healthcare applications. Wiley, pp 103\u2013120. https:\/\/doi.org\/10.1002\/9781119792611.ch7"},{"issue":"4","key":"11012_CR22","doi-asserted-by":"crossref","first-page":"47","DOI":"10.5815\/ijigsp.2021.04.04","volume":"13","author":"MR Ahmed","year":"2021","unstructured":"Ahmed MR (2021) Leveraging convolutional neural network and transfer learning for cotton plant and leaf disease recognition. Int J Image Graph Signal Process 13(4):47\u201362","journal-title":"Int J Image Graph Signal Process"},{"key":"11012_CR23","doi-asserted-by":"crossref","unstructured":"Arathi, B. and Dulhare, U.N., 2023, March. Classification of cotton leaf diseases using transfer learning-DenseNet-121. In Proceedings of third international conference on advances in computer engineering and communication systems: ICACECS 2022 (pp. 393\u2013405). Singapore: Springer Nature Singapore.","DOI":"10.1007\/978-981-19-9228-5_33"},{"key":"11012_CR24","first-page":"169","volume":"8","author":"S Batmavady","year":"2019","unstructured":"Batmavady S, Samundeeswari S (2019) Detection of cotton leaf diseases using image processing. Int J Recent Technol Eng. 8:169\u201373","journal-title":"Int J Recent Technol Eng."},{"key":"11012_CR25","doi-asserted-by":"crossref","unstructured":"Sarangdhar, A. A., & Pawar, V. R. (2017, April). Machine learning regression technique for cotton leaf disease detection and controlling using IoT. In 2017 international conference of electronics, communication and aerospace technology (ICECA) (Vol. 2, pp. 449\u2013454). IEEE.","DOI":"10.1109\/ICECA.2017.8212855"},{"issue":"8","key":"11012_CR26","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.21475\/ajcs.21.15.08.p3256","volume":"15","author":"RF Caldeira","year":"2021","unstructured":"Caldeira RF, Santiago WE, Teruel B (2021) Soft computational techniques to identify cotton leaf damage. Aust J Crop Sci 15(8):1177\u20131185","journal-title":"Aust J Crop Sci"},{"issue":"6","key":"11012_CR27","doi-asserted-by":"crossref","first-page":"5693","DOI":"10.1007\/s11063-022-10880-z","volume":"54","author":"JA Ruth","year":"2022","unstructured":"Ruth JA, Uma R, Meenakshi A, Ramkumar P (2022) Meta-heuristic based deep learning model for leaf diseases detection. Neural Process Lett 54(6):5693\u20135709","journal-title":"Neural Process Lett"},{"key":"11012_CR28","doi-asserted-by":"crossref","first-page":"106128","DOI":"10.1016\/j.asoc.2020.106128","volume":"89","author":"Y Zhao","year":"2020","unstructured":"Zhao Y, Liu L, Xie C, Wang R, Wang F, Bu Y, Zhang S (2020) An effective automatic system deployed in agricultural internet of things using multi-context fusion network towards crop disease recognition in the wild. Appl Soft Comput 89:106128","journal-title":"Appl Soft Comput"},{"key":"11012_CR29","doi-asserted-by":"crossref","unstructured":"Mekha, P., & Teeyasuksaet, N. (2021, March). \u201cImage classification of rice leaf diseases using random forest algorithm\u201d, In 2021 joint international conference on digital arts, media and technology with ECTI northern section conference on electrical, electronics, computer and telecommunication engineering, pp. 165\u2013169, IEEE.","DOI":"10.1109\/ECTIDAMTNCON51128.2021.9425696"},{"issue":"107093","key":"11012_CR30","first-page":"1","volume":"198","author":"AI Khan","year":"2022","unstructured":"Khan AI, Quadri SMK, Banday S, Shah JL (2022) Deep diagnosis: a real-time apple leaf disease detection system based on deep learning. Comput Electron Agric 198(107093):1\u201312","journal-title":"Comput Electron Agric"},{"issue":"8","key":"11012_CR31","doi-asserted-by":"crossref","first-page":"12065","DOI":"10.1007\/s11042-022-13737-8","volume":"82","author":"H Mustafa","year":"2023","unstructured":"Mustafa H, Umer M, Hafeez U, Hameed A, Sohaib A, Ullah S, Madni HA (2023) Pepper bell leaf disease detection and classification using optimized convolutional neural network. Multimedia Tools Appl 82(8):12065\u201312080","journal-title":"Multimedia Tools Appl"},{"issue":"107164","key":"11012_CR32","first-page":"1","volume":"103","author":"F Saeed","year":"2021","unstructured":"Saeed F, Khan MA, Sharif M, Mittal M, Goyal LM, 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):1\u201315","journal-title":"Appl Soft Comput"},{"issue":"6","key":"11012_CR33","first-page":"2305","volume":"13","author":"B Nerkar","year":"2021","unstructured":"Nerkar B, Talbar S (2021) Cross-dataset learning for performance improvement of leaf disease detection using reinforced generative adversarial networks. Int J Inf Technol 13(6):2305\u20132312","journal-title":"Int J Inf Technol"},{"key":"11012_CR34","doi-asserted-by":"crossref","first-page":"1382416","DOI":"10.3389\/fpls.2024.1382416","volume":"15","author":"V Pandiyaraju","year":"2024","unstructured":"Pandiyaraju V, SenthilKumar AM, Praveen Joe IR, Venkatraman S, Pavan Kumar S, Aravintakshan S, Abeshek A, Kannan A (2024) Improved tomato leaf disease classification through adaptive ensemble models with exponential moving average fusion and enhanced weighted gradient optimization. Front Plant Sci 15:1382416","journal-title":"Front Plant Sci"},{"key":"11012_CR35","doi-asserted-by":"crossref","first-page":"105210","DOI":"10.1016\/j.engappai.2022.105210","volume":"115","author":"P Kaur","year":"2022","unstructured":"Kaur P, Harnal S, Gautam V, Singh MP, Singh SP (2022) An approach for characterization of infected area in tomato leaf disease based on deep learning and object detection technique. Eng Appl Artif Intell 115:105210","journal-title":"Eng Appl Artif Intell"},{"issue":"2","key":"11012_CR36","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1007\/s13369-022-06851-0","volume":"48","author":"J He","year":"2023","unstructured":"He J, Liu T, Li L, Hu Y, Zhou G (2023) Mfaster R-CNN for maize leaf diseases detection based on machine vision. Arab J Sci Eng 48(2):1437\u20131449","journal-title":"Arab J Sci Eng"},{"key":"11012_CR37","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1007\/s11042-020-09567-1","volume":"80","author":"S Mukhopadhyay","year":"2021","unstructured":"Mukhopadhyay S, Paul M, Pal R, De D (2021) Tea leaf disease detection using multi-objective image segmentation. Multimedia Tools Appl 80:753\u2013771","journal-title":"Multimedia Tools Appl"},{"issue":"2","key":"11012_CR38","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1007\/s41315-021-00174-3","volume":"5","author":"SH Abed","year":"2021","unstructured":"Abed SH, Al-Waisy AS, Mohammed HJ, Al-Fahdawi S (2021) A modern deep learning framework in robot vision for automated bean leaves diseases detection. Int J Intell Robot Appl 5(2):235\u2013251","journal-title":"Int J Intell Robot Appl"},{"issue":"3","key":"11012_CR39","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1007\/s41348-021-00465-8","volume":"129","author":"S Vallabhajosyula","year":"2022","unstructured":"Vallabhajosyula S, Sistla V, Kolli VKK (2022) Transfer learning-based deep ensemble neural network for plant leaf disease detection. J Plant Dis Prot 129(3):545\u2013558","journal-title":"J Plant Dis Prot"},{"issue":"2","key":"11012_CR40","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1007\/s40030-022-00628-2","volume":"103","author":"K Seetharaman","year":"2022","unstructured":"Seetharaman K, Mahendran T (2022) Leaf disease detection in banana plant using gabor extraction and region-based convolution neural network (RCNN). J Inst Eng (India) Ser A 103(2):501\u2013507. https:\/\/doi.org\/10.1007\/s40030-022-00628-2","journal-title":"J Inst Eng (India) Ser A"},{"key":"11012_CR41","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1007\/s11277-021-08734-3","volume":"121","author":"SS Chouhan","year":"2021","unstructured":"Chouhan SS, Singh UP, Jain S (2021) Automated plant leaf disease detection and classification using fuzzy based function network. Wireless Pers Commun 121:1757\u20131779","journal-title":"Wireless Pers Commun"},{"issue":"4","key":"11012_CR42","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1080\/08839514.2017.1315516","volume":"31","author":"M Brahimi","year":"2017","unstructured":"Brahimi M, Boukhalfa K, Moussaoui A (2017) Deep learning for tomato diseases: classification and symptoms visualization. Appl Artif Intell 31(4):299\u2013315","journal-title":"Appl Artif Intell"},{"key":"11012_CR43","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.compag.2017.02.026","volume":"136","author":"AE Hassanien","year":"2017","unstructured":"Hassanien AE, Gaber T, Mokhtar U, Hefny H (2017) An improved moth flame optimization algorithm based on rough sets for tomato diseases detection. Comput Electron Agric 136:86\u201396","journal-title":"Comput Electron Agric"},{"key":"11012_CR44","unstructured":"Ghoury, S., Sungur, C., & Durdu, A. (2019, April). \u201cReal-time diseases detection of grape and grape leaves using faster R-CNN and SSD mobilenet architectures\u201d, In\u00a0International conference on advanced technologies, computer engineering and science (ICATCES 2019), pp. 39\u201344."},{"key":"11012_CR45","doi-asserted-by":"crossref","unstructured":"Devi, R. D., Nandhini, S. A., Hemalatha, R., & Radha, S. (2019, March). \u201cIoT enabled efficient detection and classification of plant diseases for agricultural applications\u201d. In\u00a02019 international conference on wireless communications signal processing and networking (WiSPNET), pp. 447\u2013451, IEEE.","DOI":"10.1109\/WiSPNET45539.2019.9032727"},{"issue":"105146","key":"11012_CR46","first-page":"1","volume":"168","author":"Y Zhong","year":"2020","unstructured":"Zhong Y, Zhao M (2020) Research on deep learning in apple leaf disease recognition. Comput Electron Agric 168(105146):1\u20136","journal-title":"Comput Electron Agric"},{"key":"11012_CR47","doi-asserted-by":"crossref","first-page":"631","DOI":"10.5194\/isprs-archives-XLII-3-W6-631-2019","volume":"42","author":"VK Shrivastava","year":"2019","unstructured":"Shrivastava VK, Pradhan MK, Minz S, Thakur MP (2019) Rice plant disease classification using transfer learning of deep convolution neural network. Int Arch Photogramm Remote Sens Spat Inf Sci 42:631\u2013635","journal-title":"Int Arch Photogramm Remote Sens Spat Inf Sci"},{"issue":"3","key":"11012_CR48","doi-asserted-by":"publisher","first-page":"67","DOI":"10.3390\/a13030067","volume":"13","author":"N Bacanin","year":"2020","unstructured":"Bacanin N, Bezdan T, Tuba E, Strumberger I, Tuba M (2020) Optimizing convolutional neural network hyperparameters by enhanced swarm intelligence metaheuristics. Algorithms 13(3):67. https:\/\/doi.org\/10.3390\/a13030067","journal-title":"Algorithms"},{"key":"11012_CR49","doi-asserted-by":"crossref","first-page":"9237136","DOI":"10.34133\/2019\/9237136","volume":"2019","author":"Y Toda","year":"2019","unstructured":"Toda Y, Okura F (2019) How convolutional neural networks diagnose plant disease. Plant Pheno 2019:9237136","journal-title":"Plant Pheno"},{"issue":"100616","key":"11012_CR50","first-page":"1","volume":"52","author":"A Darwish","year":"2020","unstructured":"Darwish A, Ezzat D, Hassanien AE (2020) An optimized model based on convolutional neural networks and orthogonal learning particle swarm optimization algorithm for plant diseases diagnosis. Swarm Evol Comput 52(100616):1\u201312","journal-title":"Swarm Evol Comput"},{"key":"11012_CR51","first-page":"1","volume":"79","author":"J Amara","year":"2017","unstructured":"Amara J, Bouaziz B, Algergawy A (2017) \u201cA deep learning-based approach for banana leaf diseases classification\u201d, data bank system for business, technologies and Web (BTW 2017)-workshop band. Lecture Notes Inform 79:1\u201310","journal-title":"Lecture Notes Inform"},{"key":"11012_CR52","doi-asserted-by":"crossref","unstructured":"Chouhan, S. S., Singh, U. P., Kaul, A., & Jain, S. (2019, November). \u201cA data repository of leaf images: practice towards plant conservation with plant pathology\u201d. In\u00a02019 4th international conference on information systems and computer networks (ISCON), pp. 700\u2013707, IEEE.","DOI":"10.1109\/ISCON47742.2019.9036158"},{"key":"11012_CR53","doi-asserted-by":"crossref","first-page":"49","DOI":"10.14419\/ijet.v7i4.11.20687","volume":"7","author":"E Fujita","year":"2018","unstructured":"Fujita E, Uga H, Kagiwada S, Iyatomi H (2018) A practical plant diagnosis system for field leaf images and feature visualization. Int J Eng Technol 7:49\u201354","journal-title":"Int J Eng Technol"},{"issue":"4","key":"11012_CR54","first-page":"566","volume":"7","author":"P Sharma","year":"2020","unstructured":"Sharma P, Berwal YPS, Ghai W (2020) Performance analysis of deep learning CNN models for disease detection in plants using image segmentation. Inf Process Agric 7(4):566\u2013574","journal-title":"Inf Process Agric"},{"key":"11012_CR55","doi-asserted-by":"crossref","first-page":"12659","DOI":"10.1007\/s00500-020-04707-z","volume":"24","author":"CP Saranya","year":"2020","unstructured":"Saranya CP, Nagarajan N (2020) Efficient agricultural yield prediction using metaheuristic optimized artificial neural network using Hadoop framework. Soft Comput 24:12659\u201312669","journal-title":"Soft Comput"},{"issue":"1","key":"11012_CR56","first-page":"1571936","volume":"2020","author":"T Korohou","year":"2020","unstructured":"Korohou T, Okinda C, Li H, Cao Y, Nyalala I, Huo L, Ding Q (2020) Wheat grain yield estimation based on image morphological properties and wheat biomass. J Sens 2020(1):1571936","journal-title":"J Sens"},{"issue":"3","key":"11012_CR57","first-page":"2711","volume":"43","author":"B Anusha","year":"2022","unstructured":"Anusha B, Geetha P, Kannan A (2022) Parkinson\u2019s disease identification in homo sapiens based on hybrid ResNet-SVM and ResNet-fuzzy SVM models. J Intell Fuzzy Syst 43(3):2711\u20132729","journal-title":"J Intell Fuzzy Syst"},{"key":"11012_CR58","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1007\/978-981-97-1111-6_25","volume-title":"Recent advancements in artificial intelligence: proceedings of ICRAAI 2023","author":"A Anushya","year":"2024","unstructured":"Anushya A, Shiwani S (2024) Identification of banana leaf diseases: a collaboration of deep learning models. In: Nayak Richi, Mittal Namita, Kumar Manoj, Polkowski Zdzislaw, Khunteta Ajay (eds) Recent advancements in artificial intelligence: proceedings of ICRAAI 2023. Springer Nature, Singapore, pp 309\u2013315. https:\/\/doi.org\/10.1007\/978-981-97-1111-6_25"},{"key":"11012_CR59","doi-asserted-by":"publisher","first-page":"110534","DOI":"10.1016\/j.asoc.2023.110534","volume":"145","author":"C Sarkar","year":"2023","unstructured":"Sarkar C, Gupta D, Gupta U, Hazarika BB (2023) Leaf disease detection using machine learning and deep learning: review and challenges. Appl Soft Comput 145:110534. https:\/\/doi.org\/10.1016\/j.asoc.2023.110534","journal-title":"Appl Soft Comput"},{"issue":"6","key":"11012_CR60","first-page":"12383","volume":"40","author":"SK Noon","year":"2021","unstructured":"Noon SK, Amjad M, Ali Qureshi M, Mannan A (2021) Computationally light deep learning framework to recognize cotton leaf diseases. J Intell Fuzzy Syst 40(6):12383\u201312398","journal-title":"J Intell Fuzzy Syst"},{"issue":"106128","key":"11012_CR61","first-page":"1","volume":"89","author":"Y Zhao","year":"2020","unstructured":"Zhao Y, Liu L, Xie C, Wang R, Wang F, Bu Y, Zhang S (2020) An effective automatic system deployed in agricultural internet of things using multi-context fusion network towards crop disease recognition in the wild. Appl Soft Comput 89(106128):1\u20139","journal-title":"Appl Soft Comput"},{"issue":"6","key":"11012_CR62","first-page":"4637","volume":"5","author":"P Revathi","year":"2014","unstructured":"Revathi P, Hemalatha M (2014) Identification of cotton diseases based on cross information gain deep forward neural network classifier with PSO feature selection. Int J Eng Technol 5(6):4637\u20134642","journal-title":"Int J Eng Technol"},{"key":"11012_CR63","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273\u2013297","journal-title":"Mach Learn"},{"key":"11012_CR64","doi-asserted-by":"crossref","unstructured":"Cristianini, N., Shawe-Taylor, J.: \u201cAn introduction to support vector machines and other kernel-based learning methods\u201d, Cambridge University Press, 2000.","DOI":"10.1017\/CBO9780511801389"},{"key":"11012_CR65","unstructured":"Spyder Python 3.2 environment: https:\/\/www.spyder-ide.org\/"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11012-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11012-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11012-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T08:13:48Z","timestamp":1746260028000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11012-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,20]]},"references-count":65,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["11012"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11012-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,20]]},"assertion":[{"value":"27 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 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 of this work declare that they have no conflicts of interest, financial, personal, or otherwise, that could have influenced the research, and any future potential conflicts will be disclosed promptly and transparently.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and informed consent."}}]}}