{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T20:29:46Z","timestamp":1782592186120,"version":"3.54.5"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"25","license":[{"start":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T00:00:00Z","timestamp":1680048000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T00:00:00Z","timestamp":1680048000000},"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"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s11042-023-14914-z","type":"journal-article","created":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T08:02:56Z","timestamp":1680076976000},"page":"39481-39501","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Germinative paddy seed identification using deep convolutional neural network"],"prefix":"10.1007","volume":"82","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5809-552X","authenticated-orcid":false,"given":"Mohammad Aminul","family":"Islam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9368-6942","authenticated-orcid":false,"given":"Md. Rakib","family":"Hassan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1572-8606","authenticated-orcid":false,"given":"Machbah","family":"Uddin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9011-708X","authenticated-orcid":false,"given":"Md","family":"Shajalal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,3,29]]},"reference":[{"key":"14914_CR1","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.biosystemseng.2015.08.003","volume":"139","author":"A Aakif","year":"2015","unstructured":"Aakif A, Khan MF (2015) Automatic classification of plants based on their leaves. Biosyst Eng 139:66\u201375","journal-title":"Biosyst Eng"},{"issue":"8","key":"14914_CR2","first-page":"5602","volume":"34","author":"R Akhter","year":"2022","unstructured":"Akhter R, Sofi SA (2022) Precision agriculture using iot data analytics and machine learning. J King Saud University-Comput Inf Sci 34(8):5602\u20135618","journal-title":"J King Saud University-Comput Inf Sci"},{"issue":"5","key":"14914_CR3","doi-asserted-by":"publisher","first-page":"331","DOI":"10.3923\/tasr.2012.331.349","volume":"7","author":"CO Akinbile","year":"2012","unstructured":"Akinbile CO, Haque AMM (2012) Arsenic contamination in irrigation water for rice production in bangladesh: a review. Trends Appl Sci Res 7(5):331","journal-title":"Trends Appl Sci Res"},{"issue":"35","key":"14914_CR4","first-page":"25763","volume":"79","author":"T Akram","year":"2020","unstructured":"Akram T, Sharif M, Saba T, et al. (2020) Fruits diseases classification: exploiting a hierarchical framework for deep features fusion and selection. Multimed Tools Appl 79(35):25763\u201325783","journal-title":"Multimed Tools Appl"},{"issue":"4","key":"14914_CR5","doi-asserted-by":"publisher","first-page":"5761","DOI":"10.3233\/JIFS-189415","volume":"40","author":"JA Alzubi","year":"2021","unstructured":"Alzubi JA, Jain R, Nagrath P, Satapathy S, Taneja S, Gupta P (2021) Deep image captioning using an ensemble of cnn and lstm based deep neural networks. J Intell Fuzzy Syst 40(4):5761\u20135769","journal-title":"J Intell Fuzzy Syst"},{"issue":"4","key":"14914_CR6","doi-asserted-by":"publisher","first-page":"2369","DOI":"10.1007\/s10586-021-03459-1","volume":"25","author":"OA Alzubi","year":"2022","unstructured":"Alzubi OA, Alzubi JA, Al-Zoubi A, Hassonah MA, Kose U (2022) An efficient malware detection approach with feature weighting based on harris hawks optimization. Clust Comput 25(4):2369\u20132387","journal-title":"Clust Comput"},{"issue":"1","key":"14914_CR7","first-page":"47","volume":"6","author":"BS Anami","year":"2019","unstructured":"Anami BS, Malvade NN, Palaiah S (2019) Automated recognition and classification of adulteration levels from bulk paddy grain samples. Inf Process Agric 6(1):47\u201360","journal-title":"Inf Process Agric"},{"key":"14914_CR8","first-page":"100109","volume":"3","author":"N Ansari","year":"2021","unstructured":"Ansari N, Ratri SS, Jahan A, Ashik-E-Rabbani M, Rahman A (2021) Inspection of paddy seed varietal purity using machine vision and multivariate analysis. J Agric Food Res 3:100109","journal-title":"J Agric Food Res"},{"issue":"11","key":"14914_CR9","doi-asserted-by":"publisher","first-page":"1801","DOI":"10.3390\/agriculture12111801","volume":"12","author":"RC Bernardes","year":"2022","unstructured":"Bernardes RC, Medeiros AD, da Silva L, Cantoni L, Martins GF, Mastrangelo T, Novikov A, Mastrangelo CB (2022) Deep-learning approach for fusarium head blight detection in wheat seeds using low-cost imaging technology. Agriculture 12(11):1801","journal-title":"Agriculture"},{"key":"14914_CR10","doi-asserted-by":"publisher","first-page":"101607","DOI":"10.1016\/j.techsoc.2021.101607","volume":"66","author":"AA Chandio","year":"2021","unstructured":"Chandio AA, Jiang Y, Ahmad F, Adhikari S, Ain QU (2021) Assessing the impacts of climatic and technological factors on rice production: empirical evidence from nepal. Technol Soc 66:101607","journal-title":"Technol Soc"},{"issue":"41","key":"14914_CR11","doi-asserted-by":"publisher","first-page":"31497","DOI":"10.1007\/s11042-020-09669-w","volume":"79","author":"J Chen","year":"2020","unstructured":"Chen J, Zhang D, Nanehkaran YA (2020) Identifying plant diseases using deep transfer learning and enhanced lightweight network. Multimed Tools Appl 79(41):31497\u201331515","journal-title":"Multimed Tools Appl"},{"key":"14914_CR12","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/j.compag.2017.08.005","volume":"141","author":"X Cheng","year":"2017","unstructured":"Cheng X, Zhang Y, Chen Y, Wu Y, Yue Y (2017) Pest identification via deep residual learning in complex background. Comput Electron Agric 141:351\u2013356","journal-title":"Comput Electron Agric"},{"key":"14914_CR13","doi-asserted-by":"publisher","first-page":"107530","DOI":"10.1016\/j.agwat.2022.107530","volume":"264","author":"M Cheng","year":"2022","unstructured":"Cheng M, Jiao X, Liu Y, Shao M, Yu X, Bai Y, Wang Z, Wang S, Tuohuti N, Liu S et al (2022) Estimation of soil moisture content under high maize canopy coverage from uav multimodal data and machine learning. Agric Water Manag 264:107530","journal-title":"Agric Water Manag"},{"key":"14914_CR14","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.future.2021.11.013","volume":"129","author":"M Cordeiro","year":"2022","unstructured":"Cordeiro M, Markert C, Ara\u00fajo SS, Campos NGS, Gondim RS, da Silva TLC, da Rocha AR (2022) Towards smart farming: fog-enabled intelligent irrigation system using deep neural networks. Futur Gener Comput Syst 129:115\u2013124","journal-title":"Futur Gener Comput Syst"},{"issue":"15","key":"14914_CR15","doi-asserted-by":"publisher","first-page":"19951","DOI":"10.1007\/s11042-017-5445-8","volume":"77","author":"G Dhingra","year":"2018","unstructured":"Dhingra G, Kumar V, Joshi HD (2018) Study of digital image processing techniques for leaf disease detection and classification. Multimed Tools Appl 77(15):19951\u201320000","journal-title":"Multimed Tools Appl"},{"key":"14914_CR16","doi-asserted-by":"crossref","unstructured":"Duong H-T, Hoang VT (2019) Dimensionality reduction based on feature selection for rice varieties recognition. In: 2019 4th International conference on information technology (inCIT). IEEE, pp 199\u2013202","DOI":"10.1109\/INCIT.2019.8912121"},{"key":"14914_CR17","doi-asserted-by":"crossref","unstructured":"Durai S, Mahesh C, Sujithra T, Shyamalakumari C (2022) Germination prediction system for rice seed using cnn pre-trained models. In: 2022 International conference on advances in computing, communication and applied informatics (ACCAI). IEEE, pp 1\u20139","DOI":"10.1109\/ACCAI53970.2022.9752611"},{"key":"14914_CR18","doi-asserted-by":"crossref","unstructured":"Farooq M, Basra SMA, Wahid A, Khaliq A, Kobayashi N (2009) Rice seed invigoration: a review. In: Organic farming, pest control and remediation of soil pollutants. Springer, pp 137\u2013175","DOI":"10.1007\/978-1-4020-9654-9_9"},{"key":"14914_CR19","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"issue":"1","key":"14914_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.37936\/ecti-cit.2020141.204170","volume":"14","author":"VT Hoang","year":"2020","unstructured":"Hoang VT, Hoai DPV, Surinwarangkoon T, Duong H-T, Meethongjan K (2020) A comparative study of rice variety classification based on deep learning and hand-crafted features. ECTI Trans Comput Inf Technol (ECTI-CIT) 14 (1):1\u201310","journal-title":"ECTI Trans Comput Inf Technol (ECTI-CIT)"},{"key":"14914_CR21","doi-asserted-by":"crossref","unstructured":"Hong PTT, Hai TTT, Hoang VT, Hai V, Nguyen TT et al (2015) Comparative study on vision based rice seed varieties identification. In: 2015 7th International conference on knowledge and systems engineering (KSE). IEEE, pp 377\u2013382","DOI":"10.1109\/KSE.2015.46"},{"key":"14914_CR22","doi-asserted-by":"crossref","unstructured":"Jaithavil D, Triamlumlerd S, Pracha M (2022) Paddy seed variety classification using transfer learning based on deep learning. In: 2022 International electrical engineering congress (iEECON). IEEE, pp 1\u20134","DOI":"10.1109\/iEECON53204.2022.9741677"},{"issue":"2","key":"14914_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3329\/brj.v19i2.28160","volume":"19","author":"MS Kabir","year":"2015","unstructured":"Kabir MS, Salam MU, Chowdhury A, Rahman NMF, Iftekharuddaula KM, Rahman MS, Rashid MH, Dipti SS, Islam A, Latif MA et al (2015) Rice vision for bangladesh: 2050 and beyond. Bangladesh Rice J 19(2):1\u201318","journal-title":"Bangladesh Rice J"},{"issue":"13","key":"14914_CR24","doi-asserted-by":"publisher","first-page":"9145","DOI":"10.1007\/s11042-018-7126-7","volume":"79","author":"S Kalaivani","year":"2020","unstructured":"Kalaivani S, Shantharajah S P, Padma Theagarajan (2020) Agricultural leaf blight disease segmentation using indices based histogram intensity segmentation approach. Multimed Tools Appl 79(13):9145\u20139159","journal-title":"Multimed Tools Appl"},{"issue":"25","key":"14914_CR25","doi-asserted-by":"publisher","first-page":"18627","DOI":"10.1007\/s11042-020-08726-8","volume":"79","author":"MA Khan","year":"2020","unstructured":"Khan MA, Akram T, Sharif M, Javed K, Raza M, Saba T (2020) An automated system for cucumber leaf diseased spot detection and classification using improved saliency method and deep features selection. Multimed Tools Appl 79(25):18627\u201318656","journal-title":"Multimed Tools Appl"},{"issue":"6","key":"14914_CR26","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.1080\/10942912.2015.1071839","volume":"19","author":"J Khazaei","year":"2016","unstructured":"Khazaei J, Golpour I, Moghaddam PA (2016) Evaluation of statistical and neural network architectures for the classification of paddy kernels using morphological features. Int J Food Prop 19(6):1227\u20131241","journal-title":"Int J Food Prop"},{"key":"14914_CR27","doi-asserted-by":"crossref","unstructured":"Khoenkaw P (2016) An image-processing based algorithm for rice seed germination rate evaluation. In: 2016 International computer science and engineering conference (ICSEC). IEEE, pp 1\u20135","DOI":"10.1109\/ICSEC.2016.7859890"},{"issue":"3","key":"14914_CR28","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1626\/pps.13.289","volume":"13","author":"T Kobata","year":"2010","unstructured":"Kobata T, Akiyama Y, Kawaoka T (2010) Convenient estimation of unfertilized grains in rice. Plant Prod Sci 13(3):289\u2013296","journal-title":"Plant Prod Sci"},{"key":"14914_CR29","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"issue":"4","key":"14914_CR30","first-page":"176","volume":"5","author":"B Lurstwut","year":"2016","unstructured":"Lurstwut B, Pornpanomchai C (2016) Rice seed germination analysis. Int J Comput Appl Technol Res 5(4):176\u2013182","journal-title":"Int J Comput Appl Technol Res"},{"issue":"5","key":"14914_CR31","first-page":"383","volume":"51","author":"B Lurstwut","year":"2017","unstructured":"Lurstwut B, Pornpanomchai C (2017) Image analysis based on color, shape and texture for rice seed (oryza sativa l.) germination evaluation. Agric Nat Res 51(5):383\u2013389","journal-title":"Agric Nat Res"},{"issue":"1","key":"14914_CR32","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1007\/s11104-022-05513-2","volume":"479","author":"M Moussafir","year":"2022","unstructured":"Moussafir M, Chaibi H, Saadane R, Chehri A, Rharras AE, Jeon G (2022) Design of efficient techniques for tomato leaf disease detection using genetic algorithm-based and deep neural networks. Plant Soil 479(1):251\u2013266","journal-title":"Plant Soil"},{"key":"14914_CR33","doi-asserted-by":"crossref","unstructured":"Movassagh AA, Alzubi JA, Gheisari M, Rahimi M, Mohan S, Abbasi AA, Nabipour N (2021) Artificial neural networks training algorithm integrating invasive weed optimization with differential evolutionary model. J Ambient Intell Humanized Comput:1\u20139","DOI":"10.1007\/s12652-020-02623-6"},{"issue":"4","key":"14914_CR34","doi-asserted-by":"publisher","first-page":"1897","DOI":"10.12928\/telkomnika.v18i4.14069","volume":"18","author":"H Nguyen-Quoc","year":"2020","unstructured":"Nguyen-Quoc H, Hoang VT (2020) Rice seed image classification based on hog descriptor with missing values imputation. TELKOMNIKA 18 (4):1897\u20131903","journal-title":"TELKOMNIKA"},{"key":"14914_CR35","doi-asserted-by":"crossref","unstructured":"Oikonomidis A, Catal C, Kassahun A (2022) Hybrid deep learning-based models for crop yield prediction. Appl Artif Intell:1\u201318","DOI":"10.1080\/08839514.2022.2031823"},{"key":"14914_CR36","doi-asserted-by":"publisher","first-page":"104100","DOI":"10.1016\/j.infrared.2022.104100","volume":"123","author":"J Onmankhong","year":"2022","unstructured":"Onmankhong J, Ma T, Inagaki T, Sirisomboon P, Tsuchikawa S (2022) Cognitive spectroscopy for the classification of rice varieties: a comparison of machine learning and deep learning approaches in analysing long-wave near-infrared hyperspectral images of brown and milled samples. Infrared Phys Technol 123:104100","journal-title":"Infrared Phys Technol"},{"key":"14914_CR37","doi-asserted-by":"crossref","unstructured":"Pan Y, Huang W, Lin Z, Zhu W, Zhou J, Wong J, Ding Z (2015) Brain tumor grading based on neural networks and convolutional neural networks. In: 2015 37th Annual international conference of the ieee engineering in medicine and biology society (EMBC). IEEE, pp 699\u2013702","DOI":"10.1109\/EMBC.2015.7318458"},{"key":"14914_CR38","doi-asserted-by":"crossref","unstructured":"Subramanian M, Shanmugavadivel K, Nandhini PS (2022) On fine-tuning deep learning models using transfer learning and hyper-parameters optimization for disease identification in maize leaves. Neural Comput Applic:1\u201318","DOI":"10.1007\/s00521-022-07246-w"},{"key":"14914_CR39","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"14914_CR40","unstructured":"Uddin M, Hassan M et al (2022a) A novel feature based algorithm for soil type classification. Complex Intell Syst:1\u201317"},{"issue":"1","key":"14914_CR41","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1007\/s40747-021-00545-0","volume":"8","author":"M Uddin","year":"2022","unstructured":"Uddin M, Islam MA, Shajalal M, Hossain MA, Yousuf M, Iftekhar S (2022b) Paddy seed variety identification using t20-hog and haralick textural features. Complex Intell Syst 8(1):657\u2013671","journal-title":"Complex Intell Syst"},{"key":"14914_CR42","doi-asserted-by":"crossref","unstructured":"Vaishnnave M P, Manivannan R (2022) An empirical study of crop yield prediction using reinforcement learning. Artif Intell Tech Wirel Commun Netw:47\u201358","DOI":"10.1002\/9781119821809.ch4"},{"key":"14914_CR43","doi-asserted-by":"publisher","first-page":"106623","DOI":"10.1016\/j.compag.2021.106623","volume":"192","author":"L Wang","year":"2022","unstructured":"Wang L, Fang S, Pei Z, Wu D, Zhu Y, Zhuo W (2022) Developing machine learning models with multisource inputs for improved land surface soil moisture in china. Comput Electron Agric 192:106623","journal-title":"Comput Electron Agric"},{"key":"14914_CR44","doi-asserted-by":"publisher","first-page":"106805","DOI":"10.1016\/j.compag.2022.106805","volume":"195","author":"H Yu","year":"2022","unstructured":"Yu H, Liu J, Chen C, Heidari AA, Zhang Q, Chen H (2022) Optimized deep residual network system for diagnosing tomato pests. Comput Electron Agric 195:106805","journal-title":"Comput Electron Agric"},{"issue":"21","key":"14914_CR45","doi-asserted-by":"publisher","first-page":"14539","DOI":"10.1007\/s11042-018-7092-0","volume":"79","author":"J Zhu","year":"2020","unstructured":"Zhu J, Wu A, Wang X, Zhang H (2020) Identification of grape diseases using image analysis and bp neural networks. Multimed Tools Appl 79 (21):14539\u201314551","journal-title":"Multimed Tools Appl"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-14914-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-14914-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-14914-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T10:23:11Z","timestamp":1696933391000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-14914-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,29]]},"references-count":45,"journal-issue":{"issue":"25","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["14914"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-14914-z","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,29]]},"assertion":[{"value":"23 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 March 2023","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":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}