{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T22:40:57Z","timestamp":1783636857812,"version":"3.55.0"},"reference-count":70,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013091","name":"Science and Technology Major Project of Guangxi","doi-asserted-by":"publisher","award":["Gui Ke 2018-266-Z01"],"award-info":[{"award-number":["Gui Ke 2018-266-Z01"]}],"id":[{"id":"10.13039\/501100013091","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31760342"],"award-info":[{"award-number":["31760342"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31760603"],"award-info":[{"award-number":["31760603"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sugarcane is the main industrial crop for sugar production, and its growth status is closely related to fertilizer, water, and light input. Unmanned aerial vehicle (UAV)-based multispectral imagery is widely used for high-throughput phenotyping, since it can rapidly predict crop vigor at field scale. This study focused on the potential of drone multispectral images in predicting canopy nitrogen concentration (CNC) and irrigation levels for sugarcane. An experiment was carried out in a sugarcane field with three irrigation levels and five fertilizer levels. Multispectral images at an altitude of 40 m were acquired during the elongating stage. Partial least square (PLS), backpropagation neural network (BPNN), and extreme learning machine (ELM) were adopted to establish CNC prediction models based on various combinations of band reflectance and vegetation indices. The simple ratio pigment index (SRPI), normalized pigment chlorophyll index (NPCI), and normalized green-blue difference index (NGBDI) were selected as model inputs due to their higher grey relational degree with the CNC and lower correlation between one another. The PLS model based on the five-band reflectance and the three vegetation indices achieved the best accuracy (Rv = 0.79, RMSEv = 0.11). Support vector machine (SVM) and BPNN were then used to classify the irrigation levels based on five spectral features which had high correlations with irrigation levels. SVM reached a higher accuracy of 80.6%. The results of this study demonstrated that high resolution multispectral images could provide effective information for CNC prediction and water irrigation level recognition for sugarcane crop.<\/jats:p>","DOI":"10.3390\/s22072711","type":"journal-article","created":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T21:23:55Z","timestamp":1648848235000},"page":"2711","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3449-4980","authenticated-orcid":false,"given":"Xiuhua","family":"Li","sequence":"first","affiliation":[{"name":"Guangxi Key Laboratory of Sugarcane Biology, Guangxi University, Nanning 530004, China"},{"name":"School of Electrical Engineering, Guangxi University, Nanning 530004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxuan","family":"Ba","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Guangxi University, Nanning 530004, China"},{"name":"Beijing Institute of Remote Sensing Equipment, Beijing 100854, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3138-3422","authenticated-orcid":false,"given":"Muqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Sugarcane Biology, Guangxi University, Nanning 530004, China"},{"name":"IRREC-IFAS, University of Florida, Fort Pierce, FL 34945, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengling","family":"Nong","sequence":"additional","affiliation":[{"name":"School of Agriculture, Guangxi University, Nanning 530004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1079-118X","authenticated-orcid":false,"given":"Ce","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN 55108, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3144-1883","authenticated-orcid":false,"given":"Shimin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Sugarcane Biology, Guangxi University, Nanning 530004, China"},{"name":"School of Electrical Engineering, Guangxi University, Nanning 530004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"118191","DOI":"10.1016\/j.jclepro.2019.118191","article-title":"An analysis of the Fairtrade cane sugar small producer organizations network","volume":"240","author":"Ruggeri","year":"2019","journal-title":"J. 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