{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T22:30:29Z","timestamp":1790721029729,"version":"4.1.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T00:00:00Z","timestamp":1592438400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T00:00:00Z","timestamp":1592438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001602","name":"Science Foundation Ireland","doi-asserted-by":"crossref","award":["16\/SPP\/3296"],"award-info":[{"award-number":["16\/SPP\/3296"]}],"id":[{"id":"10.13039\/501100001602","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Origin enterprises plc"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1007\/s00521-020-05064-6","type":"journal-article","created":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T17:03:37Z","timestamp":1592499817000},"page":"1733-1743","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Crop growth stage estimation prior to canopy closure using deep learning algorithms"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5488-6116","authenticated-orcid":false,"given":"Sanaz","family":"Rasti","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chris J.","family":"Bleakley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gu\u00e9nol\u00e9 C. M.","family":"Silvestre","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N. M.","family":"Holden","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Langton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory M. P.","family":"O\u2019Hare","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,6,18]]},"reference":[{"key":"5064_CR1","unstructured":"Agarap AF (2018) Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375"},{"key":"5064_CR2","unstructured":"Agriculture (2018) H.D.B.: wheat growth guide, pp 1\u201342. https:\/\/horticulture.ahdb.org.uk\/"},{"key":"5064_CR3","first-page":"4","volume":"4","author":"Z Al-Ameen","year":"2012","unstructured":"Al-Ameen Z, Sulong G, Johar MGM, Verma N, Kumar R, Dachyar M, Alkhawlani M, Mohsen A, Singh H, Singh S et al (2012) A comprehensive study on fast image deblurring techniques. Int J Adv Sci Technol 4:4","journal-title":"Int J Adv Sci Technol"},{"key":"5064_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.fcr.2016.04.027","volume":"195","author":"ST Ata-Ul-Karim","year":"2016","unstructured":"Ata-Ul-Karim ST, Liu X, Lu Z, Yuan Z, Zhu Y, Cao W (2016) In-season estimation of rice grain yield using critical nitrogen dilution curve. Field Crops Res 195:1\u20138","journal-title":"Field Crops Res"},{"key":"5064_CR5","doi-asserted-by":"crossref","unstructured":"Bansal R, Raj G, Choudhury T (2016) Blur image detection using Laplacian operator and open-cv. In: 2016 international conference system modeling and advancement in research trends (SMART). IEEE, pp 63\u201367","DOI":"10.1109\/SYSMART.2016.7894491"},{"issue":"2","key":"5064_CR6","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1023\/A:1009715923555","volume":"2","author":"CJ Burges","year":"1998","unstructured":"Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121\u2013167","journal-title":"Data Min Knowl Discov"},{"issue":"6","key":"5064_CR7","doi-asserted-by":"publisher","first-page":"1296","DOI":"10.1109\/TFUZZ.2009.2029569","volume":"17","author":"J de Jes\u00fas Rubio","year":"2009","unstructured":"de Jes\u00fas Rubio J (2009) Sofmls: online self-organizing fuzzy modified least-squares network. IEEE Trans Fuzzy Syst 17(6):1296\u20131309","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"5064_CR8","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.neucom.2016.08.150","volume":"262","author":"J de Jes\u00fas Rubio","year":"2017","unstructured":"de Jes\u00fas Rubio J (2017) Usnfis: uniform stable neuro fuzzy inference system. Neurocomputing 262:57\u201366","journal-title":"Neurocomputing"},{"key":"5064_CR9","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1016\/j.neucom.2019.10.057","volume":"390","author":"J de Jesus Rubio","year":"2019","unstructured":"de Jesus Rubio J, Pan Y, Lughofer E, Chen MY, Qiu J (2019) Fast learning of neural networks with application to big data processes. Neurocomputing 390:294\u2013296","journal-title":"Neurocomputing"},{"key":"5064_CR10","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"5064_CR11","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.biosystemseng.2016.08.024","volume":"151","author":"M Dyrmann","year":"2016","unstructured":"Dyrmann M, Karstoft H, Midtiby HS (2016) Plant species classification using deep convolutional neural network. Biosyst Eng 151:72\u201380","journal-title":"Biosyst Eng"},{"key":"5064_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3932-0","author":"D Feng","year":"2019","unstructured":"Feng D, Xu W, He Z, Zhao W, Yang M (2019) Advances in plant nutrition diagnosis based on remote sensing and computer application. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-018-3932-0","journal-title":"Neural Comput Appl"},{"key":"5064_CR13","first-page":"1","volume-title":"Colour space conversions","author":"A Ford","year":"1998","unstructured":"Ford A, Roberts A (1998) Colour space conversions. Westminster University, London, pp 1\u201331"},{"key":"5064_CR14","unstructured":"Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp 315\u2013323"},{"key":"5064_CR15","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1016\/j.compag.2016.07.003","volume":"127","author":"GL Grinblat","year":"2016","unstructured":"Grinblat GL, Uzal LC, Larese MG, Granitto PM (2016) Deep learning for plant identification using vein morphological patterns. Comput Electron Agric 127:418\u2013424","journal-title":"Comput Electron Agric"},{"issue":"1","key":"5064_CR16","doi-asserted-by":"publisher","first-page":"290","DOI":"10.3390\/rs2010290","volume":"2","author":"ER Hunt","year":"2010","unstructured":"Hunt ER, Hively WD, Fujikawa S, Linden D, Daughtry CS, McCarty G (2010) Acquisition of nir-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sens 2(1):290\u2013305","journal-title":"Remote Sens"},{"key":"5064_CR17","volume-title":"Principal component analysis","author":"I Jolliffe","year":"2011","unstructured":"Jolliffe I (2011) Principal component analysis. Springer, Berlin"},{"key":"5064_CR18","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.compag.2019.01.041","volume":"158","author":"A Kaya","year":"2019","unstructured":"Kaya A, Keceli AS, Catal C, Yalic HY, Temucin H, Tekinerdogan B (2019) Analysis of transfer learning for deep neural network based plant classification models. Comput Electron Agric 158:20\u201329","journal-title":"Comput Electron Agric"},{"key":"5064_CR19","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":"1","key":"5064_CR20","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.patcog.2013.06.012","volume":"47","author":"MG Larese","year":"2014","unstructured":"Larese MG, Nam\u00edas R, Craviotto RM, Arango MR, Gallo C, Granitto PM (2014) Automatic classification of legumes using leaf vein image features. Pattern Recognit 47(1):158\u2013168","journal-title":"Pattern Recognit"},{"issue":"7553","key":"5064_CR21","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436","journal-title":"Nature"},{"key":"5064_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patcog.2017.05.015","volume":"71","author":"SH Lee","year":"2017","unstructured":"Lee SH, Chan CS, Mayo SJ, Remagnino P (2017) How deep learning extracts and learns leaf features for plant classification. Pattern Recognit 71:1\u201313","journal-title":"Pattern Recognit"},{"key":"5064_CR23","doi-asserted-by":"crossref","unstructured":"Luo Y, Tang X (2008) Photo and video quality evaluation: focusing on the subject. In: European conference on computer vision. Springer, pp 386\u2013399","DOI":"10.1007\/978-3-540-88690-7_29"},{"key":"5064_CR24","first-page":"67","volume":"49","author":"S Mahmoodi","year":"2009","unstructured":"Mahmoodi S, Rahimi A (2009) Estimation of critical period for weed control in corn in Iran. World Acad Sci Eng Technol 49:67\u201372","journal-title":"World Acad Sci Eng Technol"},{"key":"5064_CR25","doi-asserted-by":"publisher","first-page":"31968","DOI":"10.1109\/ACCESS.2018.2846483","volume":"6","author":"JA Meda-Campa\u00f1a","year":"2018","unstructured":"Meda-Campa\u00f1a JA (2018) On the estimation and control of nonlinear systems with parametric uncertainties and noisy outputs. IEEE Access 6:31968\u201331973","journal-title":"IEEE Access"},{"key":"5064_CR26","volume-title":"Growth stages of mono- and dicotyledonous plants","author":"U Meier","year":"1997","unstructured":"Meier U (1997) Growth stages of mono- and dicotyledonous plants. Blackwell Wissenschafts-Verlag, Berlin"},{"key":"5064_CR27","doi-asserted-by":"crossref","unstructured":"Meyer GE, Hindman TW, Laksmi K (1999) Machine vision detection parameters for plant species identification. In: Precision agriculture and biological quality, vol 3543, Int. Society for Optics and Photonics, pp 327\u2013336","DOI":"10.1117\/12.336896"},{"key":"5064_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.biosystemseng.2019.05.002","volume":"184","author":"L Quan","year":"2019","unstructured":"Quan L, Feng H, Lv Y, Wang Q, Zhang C, Liu J, Yuan Z (2019) Maize seedling detection under different growth stages and complex field environments based on an improved faster r-cnn. Biosyst Eng 184:1\u201323","journal-title":"Biosyst Eng"},{"key":"5064_CR29","doi-asserted-by":"publisher","first-page":"252","DOI":"10.3389\/fpls.2017.00252","volume":"8","author":"P Sadeghi-Tehran","year":"2017","unstructured":"Sadeghi-Tehran P, Sabermanesh K, Virlet N, Hawkesford MJ (2017) Automated method to determine two critical growth stages of wheat: heading and flowering. Front Plant Sci 8:252","journal-title":"Front Plant Sci"},{"key":"5064_CR30","unstructured":"Sekizawa H, Yamamoto N, Kawakami H, Saito T (1994) Brightness referenced color image correcting apparatus. US Patent 5,278,641"},{"issue":"5","key":"5064_CR31","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","volume":"35","author":"HC Shin","year":"2016","unstructured":"Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285\u20131298","journal-title":"IEEE Trans Med Imaging"},{"issue":"3","key":"5064_CR32","first-page":"1264","volume":"47","author":"P Sibi","year":"2013","unstructured":"Sibi P, Jones SA, Siddarth P (2013) Analysis of different activation functions using back propagation neural networks. J Theor Appl Inf Technol 47(3):1264\u20131268","journal-title":"J Theor Appl Inf Technol"},{"key":"5064_CR33","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. ArXiv Preprint arXiv:1409.1556"},{"issue":"1","key":"5064_CR34","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/S0378-4290(02)00045-X","volume":"77","author":"A Soltani","year":"2002","unstructured":"Soltani A, Galeshi S (2002) Importance of rapid canopy closure for wheat production in a temperate sub-humid environment: experimentation and simulation. Field Crops Res 77(1):17\u201330","journal-title":"Field Crops Res"},{"key":"5064_CR35","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/j.jclepro.2014.04.036","volume":"88","author":"M Srbinovska","year":"2015","unstructured":"Srbinovska M, Gavrovski C, Dimcev V, Krkoleva A, Borozan V (2015) Environmental parameters monitoring in precision agriculture using wireless sensor networks. J Clean Prod 88:297\u2013307","journal-title":"J Clean Prod"},{"issue":"2","key":"5064_CR36","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.compag.2006.06.001","volume":"53","author":"V Subramanian","year":"2006","unstructured":"Subramanian V, Burks TF, Arroyo A (2006) Development of machine vision and laser radar based autonomous vehicle guidance systems for citrus grove navigation. Comput Electron Agric 53(2):130\u2013143","journal-title":"Comput Electron Agric"},{"issue":"3","key":"5064_CR37","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1111\/aab.12145","volume":"165","author":"W Thomas","year":"2014","unstructured":"Thomas W (2014) The value of decimal cereal growth stages. Ann Appl Biol 165(3):303\u2013304","journal-title":"Ann Appl Biol"},{"key":"5064_CR38","doi-asserted-by":"crossref","unstructured":"Ting KM (2017) Confusion matrix. Encyclopedia of machine learning and data mining, pp 260\u2013260","DOI":"10.1007\/978-1-4899-7687-1_50"},{"issue":"6","key":"5064_CR39","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.1109\/TRO.2016.2603528","volume":"32","author":"P Tokekar","year":"2016","unstructured":"Tokekar P, Vander Hook J, Mulla D, Isler V (2016) Sensor planning for a symbiotic uav and ugv system for precision agriculture. IEEE Trans Robot 32(6):1498\u20131511","journal-title":"IEEE Trans Robot"},{"key":"5064_CR40","doi-asserted-by":"crossref","unstructured":"Wang J, Chen L, Zhang J, Yuan Y, Li M, Zeng W (2018) Cnn transfer learning for automatic image-based classification of crop disease. In: Chinese conference on image and graphics technologies. Springer, pp 319\u2013329","DOI":"10.1007\/978-981-13-1702-6_32"},{"key":"5064_CR41","doi-asserted-by":"publisher","DOI":"10.1007\/b95439","volume-title":"Support vector machines: theory and applications","author":"L Wang","year":"2005","unstructured":"Wang L (2005) Support vector machines: theory and applications, vol 177. Springer, Berlin"},{"key":"5064_CR42","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.compag.2012.02.009","volume":"84","author":"J Xue","year":"2012","unstructured":"Xue J, Zhang L, Grift TE (2012) Variable field-of-view machine vision based row guidance of an agricultural robot. Comput Electron Agric 84:85\u201391","journal-title":"Comput Electron Agric"},{"key":"5064_CR43","doi-asserted-by":"crossref","unstructured":"Yalcin H, Razavi S (2016) Plant classification using convolutional neural networks. In: 2016 Fifth international conference on agro-geoinformatics (agro-geoinformatics). IEEE, pp 1\u20135","DOI":"10.1109\/Agro-Geoinformatics.2016.7577698"},{"key":"5064_CR44","unstructured":"Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks? In: Advances in neural information processing systems, pp 3320\u20133328"},{"key":"5064_CR45","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.agrformet.2013.02.011","volume":"174","author":"Z Yu","year":"2013","unstructured":"Yu Z, Cao Z, Wu X, Bai X, Qin Y, Zhuo W, Xiao Y, Zhang X, Xue H (2013) Automatic image-based detection technology for two critical growth stages of maize: emergence and three-leaf stage. Agric For Meteorol 174:65\u201384","journal-title":"Agric For Meteorol"},{"key":"5064_CR46","doi-asserted-by":"crossref","unstructured":"Yudhana A, Umar R, Ayudewi FM (2019) The monitoring of corn sprouts growth using the region growing methods. In: Journal of Physics: conference series, vol 1373, IOP Publishing, p 012054","DOI":"10.1088\/1742-6596\/1373\/1\/012054"},{"issue":"6","key":"5064_CR47","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1111\/j.1365-3180.1974.tb01084.x","volume":"14","author":"JC Zadoks","year":"1974","unstructured":"Zadoks JC, Chang TT, Konzak CF (1974) A decimal code for the growth stages of cereals. Weed Res 14(6):415\u2013421","journal-title":"Weed Res"},{"key":"5064_CR48","doi-asserted-by":"crossref","unstructured":"Zainuddin Z, Manjang S, Wijaya AS, et al. (2019) Rice farming age detection use drone based on svm histogram image classification. In: Journal of Physics: conference series, vol 1198. IOP Publishing, p 092001","DOI":"10.1088\/1742-6596\/1198\/9\/092001"},{"issue":"2\u20133","key":"5064_CR49","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/S0168-1699(02)00096-0","volume":"36","author":"N Zhang","year":"2002","unstructured":"Zhang N, Wang M, Wang N (2002) Precision agriculture\u2014a worldwide overview. Comput Electron Agric 36(2\u20133):113\u2013132","journal-title":"Comput Electron Agric"},{"key":"5064_CR50","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1016\/j.compag.2019.05.020","volume":"162","author":"S Zhao","year":"2019","unstructured":"Zhao S, Zheng H, Chi M, Chai X, Liu Y (2019) Rapid yield prediction in paddy fields based on 2d image modelling of rice panicles. Comput Electron Agric 162:759\u2013766","journal-title":"Comput Electron Agric"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05064-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05064-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05064-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T23:37:51Z","timestamp":1623973071000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05064-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,18]]},"references-count":50,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["5064"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05064-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,18]]},"assertion":[{"value":"22 October 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}