[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123837-en":3,"doc-seo-123837-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123837,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Machine Learning Optimised Hyperspectral Remote Sensing Retrieves Cotton Nitrogen Status","Hyperspectral imaging spectrometers mounted on unmanned aerial vehicles provide high spatial and spectral detail for mapping cotton nitrogen status in precision agriculture. The research evaluates machine learning applied to hyperspectral datacubes collected over a mature cotton crop, enabling discrimination of crop rows and continuous spectra for derivative analysis. Nominal reflectance and derivatives link strongly to nitrogen concentration in leaf and petiole samples, outperforming traditional vegetation indices in novel spectral combinations.","remote sensing  \nArticle  \nMachine Learning Optimised Hyperspectral Remote Sensing Retrieves Cotton Nitrogen Status  \nIan J. Marang 1,*, Patrick Filippi 1, Tim B. Weaver 2, Bradley J. Evans 3, Brett M. Whelan 1, Thomas F. A. Bishop 1, Mohammed O. F. Murad 1, Dhahi Al-Shammari 1 and Guy Roth 1  \n􀀁􀀂􀀃􀀁􀀄 􀀆􀀇􀀈  \n􀀁􀀂􀀃􀀄􀀅􀀆􀀇  \nCitation: Marang, I.J.; Filippi, P.; Weaver, T.B.; Evans, B.J.; Whelan, B.M.; Bishop, T.F.A.; Murad, M.O.F.; Al-Shammari, D.; Roth, G. Machine Learning Optimised Hyperspectral Remote Sensing Retrieves Cotton Nitrogen Status. Remote Sens. 2021, 13, 1428. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)rs13081428  \nAcademic Editors: Jaime Zabalza, Jinchang Ren and Yijun Yan  \nReceived: 10 March 2021  \nAccepted: 3 April 2021  \nPublished: 7 April 2021  \nPublisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  \nCopyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Faculty of Science, School of Life and Environmental Sciences, Sydney Institute of Agriculture, The University of Sydney, Sydney, NSW 2006, Australia; patrick.fi[lippi@sydney.edu.au](lippi@sydney.edu.au) (P.F.);  \n[brett.whelan@sydney.edu.au](brett.whelan@sydney.edu.au) (B.M.W.); [thomas.bishop@sydney.edu.au](thomas.bishop@sydney.edu.au) (T.F.A.B.);  \n[mohammed.murad@sydney.edu.au](mohammed.murad@sydney.edu.au) (M.O.F.M.); [dals0705@uni.sydney.edu.au](dals0705@uni.sydney.edu.au) (D.A.-S.);  \n[guy.roth@sydney.edu.au](guy.roth@sydney.edu.au) (G.R.)  \n2 CSIRO Agriculture and Food, Australian Cotton Research Institute, Locked Bag 59, Narrabri, NSW 2390, Australia; [tim.weaver@csiro.au](tim.weaver@csiro.au)  \n3 Faculty of Science, School of Physics, The University of Sydney, Sydney, NSW 2006, Australia; [bradley.evans@sydney.edu.au](bradley.evans@sydney.edu.au)  \n* Correspondence: [ian.marang@sydney.edu.au](ian.marang@sydney.edu.au); Tel.: +61-432941663  \nAbstract: Hyperspectral imaging spectrometers mounted on unmanned aerial vehicle (UAV) can capture high spatial and spectral resolution to provide cotton crop nitrogen status for precision agriculture. The aim of this research was to explore machine learning use with hyperspectral datacubes over agricultural fields. Hyperspectral imagery was collected over a mature cotton crop, which had high spatial (~5.2 cm) and spectral (5 nm) resolution over the spectral range 475–925 nm that allowed discrimination of individual crop rows and field features as well as a continuous spectral range for calculating derivative spectra. The nominal reflectance and its derivatives clearly highlighted the different treatment blocks and were strongly related to N concentration in leaf and petiole samples, both in traditional vegetation indices (e.g., Vogelman 1, R2 = 0 .8) and novel combinations of spectra (R2 = 0 .85) . The key hyperspectral bands identified were at the red-edge inflection point (695–715 nm) . Satellite multispectral was compared against the UAV hyperspectral remote sensing's performance by testing the ability of Sentinel MSI to predict N concentration using the bands in VIS-NIR spectral region. The Sentinel 2A Green band (B3; mid-point 559.8 nm) explained the same amount of variation in N as the hyperspectral data and more than the Sentinel Red Edge Point 1 (B5; mid-point 704 .9 nm) with the lower 10 m resolution Green band reporting an R2 = 0 .85, compared with the R2 = 0 .78 of downscaled Sentinel Red Edge Point 1 at 5 m. The remaining Sentinel bands explained much lower variation (maximum was NIR at R2 = 0 .48) . Investigation of the red edge peak region in the first derivative showed strong promise with RIDAmid (R2 = 0 ","cbCaiqN1PRbeRzQo","https://ap.wps.com/l/cbCaiqN1PRbeRzQo","pdf",6188837,1,19,"English","en",105,"# Abstract\n# Introduction\n## Nitrogen importance in cotton and environmental impacts","[{\"question\":\"What was the research aim regarding cotton nitrogen status?\",\"answer\":\"To explore how machine learning can use hyperspectral datacubes to retrieve cotton nitrogen status for precision agriculture management.\"},{\"question\":\"How were the hyperspectral data collected and what resolution was achieved?\",\"answer\":\"Hyperspectral imagery was collected over mature cotton using UAV-mounted spectrometers, with high spatial (~5.2 cm) and spectral (5 nm) resolution across 475–925 nm.\"},{\"question\":\"How did Sentinel multispectral data compare with UAV hyperspectral for predicting nitrogen?\",\"answer\":\"Sentinel MSI performed well for nitrogen prediction, with the Sentinel 2A Green band explaining variation comparable to hyperspectral data, while other Sentinel bands contributed less.\"}]","Machine Learning Optimised Hyperspectral Remote Sensing Retrieves Cotton Nitrogen Status | 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