{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:00:41Z","timestamp":1775066441827,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:00:00Z","timestamp":1731542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"S\u00e3o Paulo Research Foundation (FAPESP)","award":["2013\/22435-9"],"award-info":[{"award-number":["2013\/22435-9"]}]},{"name":"Luiz de Queiroz Foundation for Agricultural Studies (FEALQ)","award":["2013\/22435-9"],"award-info":[{"award-number":["2013\/22435-9"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Nitrogen fertilization is a challenging task that usually requires intensive use of resources, such as fertilizers, management and water. This study explored the potential of VIS-NIR-SWIR remote sensing for quantifying leaf nitrogen content (LNC) in sugarcane from different regions and vegetative stages. Conducted in three regions of S\u00e3o Paulo, Brazil (Ja\u00fa, Piracicaba and Santa Maria), the research involved three experiments, one per location. The spectral data were obtained at 140, 170, 200, 230 and 260 days after cutting (DAC). From the hyperspectral data, clustering analysis was performed to identify the patterns between the spectral bands for each region where the spectral readings were made, using the Partitioning Around Medoids (PAM) algorithm. Then, the LNC values were used to generate spectral models using Partial Least Squares Regression (PLSR). Subsequently, the generalization of the models was tested with the leave-one-date-out cross-validation (LOOCV) technique. The results showed that although the variation in leaf N was small, the sensor demonstrated the ability to detect these variations. Furthermore, it was possible to determine the influence of N concentrations on the leaf spectra and how this impacted cluster formation. It was observed that the greater the average variation in N content in each cluster, the better defined and denser the groups formed were. The best time to quantify N concentrations was at 140 DAC (R2 = 0.90 and RMSE = 0.74 g kg\u22121). From LOOCV, the areas with sandier soil texture presented a lower model performance compared to areas with clayey soil, with R2 &lt; 0.54. The spatial generalization of the models recorded the best performance at 140 DAC (R2 = 0.69, RMSE = 1.18 g kg\u22121 and dr = 0.61), decreasing in accuracy at the crop-maturation stage (260 DAC), R2 of 0.05, RMSE of 1.73 g kg\u22121 and dr of 0.38. Although the technique needs further studies to be improved, our results demonstrated potential, which tends to provide support and benefits for the quantification of nutrients in sugarcane in the long term.<\/jats:p>","DOI":"10.3390\/rs16224250","type":"journal-article","created":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T10:47:53Z","timestamp":1731581273000},"page":"4250","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Spatio-Temporal Generalization of VIS-NIR-SWIR Spectral Models for Nitrogen Prediction in Sugarcane Leaves"],"prefix":"10.3390","volume":"16","author":[{"given":"Carlos Augusto Alves Cardoso","family":"Silva","sequence":"first","affiliation":[{"name":"Department of Biosystems Engineering, \u201cLuiz de Queiroz\u201d College of Agriculture, University of S\u00e3o Paulo, Piracicaba 13418900, SP, Brazil"}]},{"given":"Rodnei","family":"Rizzo","sequence":"additional","affiliation":[{"name":"Department of Exact Science, \u201cLuiz de Queiroz\u201d College of Agriculture, University of S\u00e3o Paulo, Piracicaba 13418900, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9079-8981","authenticated-orcid":false,"given":"Marcelo Andrade","family":"da Silva","sequence":"additional","affiliation":[{"name":"Department of Exact Science, \u201cLuiz de Queiroz\u201d College of Agriculture, University of S\u00e3o Paulo, Piracicaba 13418900, SP, Brazil"}]},{"given":"Matheus Lu\u00eds","family":"Caron","sequence":"additional","affiliation":[{"name":"Department of Biosystems Engineering, \u201cLuiz de Queiroz\u201d College of Agriculture, University of S\u00e3o Paulo, Piracicaba 13418900, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3461-357X","authenticated-orcid":false,"given":"Peterson Ricardo","family":"Fiorio","sequence":"additional","affiliation":[{"name":"Department of Biosystems Engineering, \u201cLuiz de Queiroz\u201d College of Agriculture, University of S\u00e3o Paulo, Piracicaba 13418900, SP, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cifuentes, J., Salazar, V.A., Cuellar, M., Castellanos, M.C., Rodr\u00edguez, J., Cruz, J.C., and Mu\u00f1oz-Camargo, C. (2021). Antioxidant and Neuroprotective Properties of Non-Centrifugal Cane Sugar and Other Sugarcane Derivatives in an In Vitro Induced Parkinson\u2019s Model. Antioxidants, 10.","DOI":"10.3390\/antiox10071040"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106266","DOI":"10.1016\/j.landusepol.2022.106266","article-title":"Agro-industrial development: Lessons from Brazil","volume":"120","author":"Medina","year":"2022","journal-title":"Land Use Policy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1007\/s12355-022-01182-8","article-title":"Integration of Bio-products and NPK Fertilizers for Increasing Productivity and Sustainability of Sugarcane-Based System in Subtropical India","volume":"25","author":"Shukla","year":"2023","journal-title":"Sugar Tech."},{"key":"ref_4","first-page":"119","article-title":"Energy Use Pattern and Scenario Change in Sugarcane (ratoon) Cultivation for Bhabar Region of Uttarakhand, India","volume":"2","author":"Singh","year":"2015","journal-title":"J. AgriSearch"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"311","DOI":"10.19045\/bspab.2016.50040","article-title":"Response of sugarcane to different doses of Zn at various growth stages","volume":"5","author":"Ismail","year":"2016","journal-title":"Pure Appl. Biol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1007\/s12355-019-00706-z","article-title":"Green Technologies for Improving Cane Sugar Productivity and Sustaining Soil Fertility in Sugarcane-Based Cropping System","volume":"21","author":"Shukla","year":"2019","journal-title":"Sugar Tech."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"181","DOI":"10.5194\/essd-9-181-2017","article-title":"Global nitrogen and phosphorus fertilizer use for agriculture production in the past half century: Shifted hot spots and nutrient imbalance","volume":"9","author":"Lu","year":"2017","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1007\/s00374-020-01484-7","article-title":"Soybean (Glycine max (L.) Merrill) intercropping with reduced nitrogen input influences rhizosphere phosphorus dynamics and phosphorus acquisition of sugarcane (Saccharum officinarum)","volume":"56","author":"Tian","year":"2020","journal-title":"Biol. Fertil. Soils"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/s10705-020-10074-w","article-title":"Nitrogen fertilizer effects on sugarcane growth, nutritional status, and productivity in tropical acid soils","volume":"117","author":"Boschiero","year":"2020","journal-title":"Nutr. Cycl. Agroecosyst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1007\/s12355-017-0566-y","article-title":"Effects of Drought Stress at Early Growth Stage on Response of Sugarcane to Different Nitrogen Application","volume":"20","author":"Dinh","year":"2018","journal-title":"Sugar Tech."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bassi, D., Menossi, M., and Mattiello, L. (2018). Nitrogen supply influences photosynthesis establishment along the sugarcane leaf. Sci. Rep., 8.","DOI":"10.1038\/s41598-018-20653-1"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1080\/1343943X.2017.1371570","article-title":"Photosynthetic response and nitrogen use efficiency of sugarcane under drought stress conditions with different nitrogen application levels","volume":"20","author":"Dinh","year":"2017","journal-title":"Plant Prod. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s002710100038","article-title":"Impact of fertilisation practices on nitrogen leaching under irrigation","volume":"20","author":"Mailhol","year":"2001","journal-title":"Irrig. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6342","DOI":"10.1080\/01431161.2012.687473","article-title":"Assessment of plant nitrogen stress in wheat (Triticum aestivum L.) through hyperspectral indices","volume":"33","author":"Ranjan","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e26819","DOI":"10.1016\/j.heliyon.2024.e26819","article-title":"Prediction of leaf nitrogen in sugarcane (Saccharum spp.) by vis-NIR-SWIR spectroradiometry","volume":"10","author":"Fiorio","year":"2024","journal-title":"Heliyon"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s12355-023-01329-1","article-title":"Application of Vegetative Indices for Leaf Nitrogen Estimation in Sugarcane Using Hyperspectral Data","volume":"26","author":"Martins","year":"2023","journal-title":"Sugar Tech."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"115278","DOI":"10.1016\/j.indcrop.2022.115278","article-title":"Estimating technological parameters and stem productivity of sugarcane treated with rock powder using a proximal spectroradiometer Vis-NIR-SWIR","volume":"186","author":"Rodrigues","year":"2022","journal-title":"Ind. Crops Prod."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e20200630","DOI":"10.1590\/0103-8478cr20200630","article-title":"Estimation of leaf nitrogen levels in sugarcane using hyperspectral models","volume":"52","author":"Barros","year":"2022","journal-title":"Ci\u00eancia Rural"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e20220543","DOI":"10.1590\/0103-8478cr20220543","article-title":"Detection of nutritional stress in sugarcane by VIS-NIR-SWIR reflectance spectroscopy","volume":"53","author":"Silva","year":"2023","journal-title":"Ci\u00eancia Rural"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tavares, M.S., Silva, C.A.A.C., Regazzo, J.R., Sardinha, E.J.D.S., da Silva, T.L., Fiorio, P.R., and Baesso, M.M. (2024). Performance of Machine Learning Models in Predicting Common Bean (Phaseolus vulgaris L.) Crop Nitrogen Using NIR Spectroscopy. Agronomy, 14.","DOI":"10.20944\/preprints202405.2063.v1"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e47632","DOI":"10.4025\/actasciagron.v43i1.47632","article-title":"Potential use of hyperspectral data to monitor sugarcane nitrogen status","volume":"43","author":"Martins","year":"2020","journal-title":"Acta Sci. Agron."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e20220008","DOI":"10.1590\/1809-4430-eng.agric.v43n3e20220008\/2023","article-title":"Effect of different nitrogen fertilization rates on the spectral response of Brachiaria brizantha cv. Marand\u00fa leaves","volume":"43","author":"Nilsson","year":"2023","journal-title":"Eng. Agr\u00edcola"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112353","DOI":"10.1016\/j.rse.2021.112353","article-title":"Field spectroscopy of canopy nitrogen concentration in temperate grasslands using a convolutional neural network","volume":"257","author":"Pullanagari","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1111\/nph.16711","article-title":"Foliar functional traits from imaging spectroscopy across biomes in eastern North America","volume":"228","author":"Wang","year":"2020","journal-title":"New Phytol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"112826","DOI":"10.1016\/j.rse.2021.112826","article-title":"Combining transfer learning and hyperspectral reflectance analysis to assess leaf nitrogen concentration across different plant species datasets","volume":"269","author":"Wan","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1127\/0941-2948\/2013\/0507","article-title":"K\u00f6ppen\u2019s climate classification map for Brazil","volume":"22","author":"Alvares","year":"2013","journal-title":"Meteorol. Z."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2388","DOI":"10.1002\/ps.5775","article-title":"Hormetic effect of glyphosate persists during the entire growth period and increases sugarcane yield","volume":"76","author":"Bortolheiro","year":"2020","journal-title":"Pest. Manag. Sci."},{"key":"ref_28","unstructured":"Rossi, M. (2024, August 23). MAPA PEDOL\u00d3GICO DO ESTADO DE S\u00c3O PAULO: REVISADO E AMPLIADO. Volume 1. S\u00e3o Paulo, Available online: www.iflorestal.sp.gov.br."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2562","DOI":"10.1109\/JSTARS.2014.2330521","article-title":"Determining the Effects of Storage on Cotton and Soybean Leaf Samples for Hyperspectral Analysis","volume":"7","author":"Lee","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e20190587","DOI":"10.1590\/0103-8478cr20190587","article-title":"Effects of storage on vis-NIR-SWIR reflectance spectra of Mombasa grass leaf samples","volume":"50","author":"Tavares","year":"2020","journal-title":"Ci\u00eancia Rural"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lu, B., Wang, X., Liu, N., He, K., Wu, K., Li, H., and Tang, X. (2020). Feasibility of NIR spectroscopy detection of moisture content in coco-peat substrate based on the optimization characteristic variables. Spectrochim. Acta A Mol. Biomol. Spectrosc., 239.","DOI":"10.1016\/j.saa.2020.118455"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and differentiation of data by simplified least squares procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3336","DOI":"10.1016\/j.eswa.2008.01.039","article-title":"A simple and fast algorithm for K-medoids clustering","volume":"36","author":"Park","year":"2009","journal-title":"Expert. Syst. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.neucom.2022.04.002","article-title":"Analysis of clustering methods for crop type mapping using satellite imagery","volume":"492","author":"Rivera","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.rse.2016.07.014","article-title":"Leaf spectral clusters as potential optical leaf functional types within California ecosystems","volume":"184","author":"Roth","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.compag.2016.01.007","article-title":"Estimating wheat biomass by combining image clustering with crop height","volume":"121","author":"Schirrmann","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J. Comput. Appl. Math."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Shahapure, K.R., and Nicholas, C. (2020, January 6\u20139). Cluster Quality Analysis Using Silhouette Score. Proceedings of the 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), Sydney, NSW, Australia.","DOI":"10.1109\/DSAA49011.2020.00096"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.saa.2017.06.048","article-title":"Influence of spectral resolution, spectral range and signal-to-noise ratio of Fourier transform infra-red spectra on identification of high explosive substances","volume":"188","author":"Banas","year":"2018","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"126717","DOI":"10.1016\/j.eja.2022.126717","article-title":"Spectral method for macro and micronutrient prediction in soybean leaves using interval partial least squares regression","volume":"143","author":"Reis","year":"2023","journal-title":"Eur. J. Agron."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"107991","DOI":"10.1016\/j.compag.2023.107991","article-title":"Hyperspectral reflectance and machine learning to monitor legume biomass and nitrogen accumulation","volume":"211","author":"Flynn","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"044505","DOI":"10.1117\/1.JRS.14.044505","article-title":"Vis\u2013NIR spectroscopy: From leaf dry mass production estimate to the prediction of macro- and micronutrients in soybean crops","volume":"14","author":"Rodrigues","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e06566","DOI":"10.1016\/j.heliyon.2021.e06566","article-title":"Estimating canopy nitrogen concentration of sugarcane crop using in situ spectroscopy","volume":"7","year":"2021","journal-title":"Heliyon"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.saa.2017.08.034","article-title":"Classification and quantitation of milk powder by near-infrared spectroscopy and mutual information-based variable selection and partial least squares","volume":"189","author":"Chen","year":"2018","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.1002\/joc.2419","article-title":"A refined index of model performance","volume":"32","author":"Willmott","year":"2012","journal-title":"Int. J. Climatol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0034-4257(02)00010-X","article-title":"Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages","volume":"81","author":"Sims","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.envexpbot.2017.06.001","article-title":"Distinct growth light and gibberellin regimes alter leaf anatomy and reveal their influence on leaf optical properties","volume":"140","author":"Falcioni","year":"2017","journal-title":"Environ. Exp. Bot."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.envexpbot.2011.08.010","article-title":"Effect of green light wavelength and intensity on photomorphogenesis and photosynthesis in Lactuca sativa","volume":"75","author":"Johkan","year":"2012","journal-title":"Environ. Exp. Bot."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"70","DOI":"10.3732\/ajb.1200354","article-title":"Contributions of green light to plant growth and development","volume":"100","author":"Wang","year":"2013","journal-title":"Am. J. Bot."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"105258","DOI":"10.1016\/j.envexpbot.2023.105258","article-title":"Chloroplast and outside-chloroplast interference of light inside leaves","volume":"208","author":"Moriwaki","year":"2023","journal-title":"Environ. Exp. Bot."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Alharbi, K., Haroun, S.A., Kazamel, A.M., Abbas, M.A., Ahmaida, S.M., AlKahtani, M., AlHusnain, L., Attia, K.A., Abdelaal, K., and Gamel, R.M. (2022). Physiological Studies and Ultrastructure of Vigna sinensis L. and Helianthus annuus L. under Varying Levels of Nitrogen Supply. Plants, 11.","DOI":"10.3390\/plants11141884"},{"key":"ref_52","first-page":"151","article-title":"Chlorophyll a fluorescence signatures of nitrogen deficient barley leaves","volume":"28","author":"Hak","year":"1993","journal-title":"Photosynthetica"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1093\/pcp\/pcp034","article-title":"Green Light Drives Leaf Photosynthesis More Efficiently than Red Light in Strong White Light: Revisiting the Enigmatic Question of Why Leaves are Green","volume":"50","author":"Terashima","year":"2009","journal-title":"Plant Cell Physiol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1128","DOI":"10.21273\/HORTSCI.50.8.1128","article-title":"Spectral Effects of Artificial Light on Plant Physiology and Secondary Metabolism: A Review","volume":"50","author":"Ouzounis","year":"2015","journal-title":"HortScience"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.21273\/HORTSCI.45.12.1809","article-title":"Blue Light-emitting Diode Light Irradiation of Seedlings Improves Seedling Quality and Growth after Transplanting in Red Leaf Lettuce","volume":"45","author":"Johkan","year":"2010","journal-title":"HortScience"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.fcr.2005.01.016","article-title":"Modelling nitrogen dynamics in sugarcane systems: Recent advances and applications","volume":"92","author":"Thorburn","year":"2005","journal-title":"Field Crops Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"108859","DOI":"10.1016\/j.fcr.2023.108859","article-title":"Retrieving canopy nitrogen concentration and aboveground biomass with deep learning for ryegrass and barley: Comparing models and determining waveband contribution","volume":"294","author":"Patel","year":"2023","journal-title":"Field Crops Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1007\/s11119-019-09661-x","article-title":"Estimation and mapping of nitrogen content in apple trees at leaf and canopy levels using hyperspectral imaging","volume":"21","author":"Ye","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Singels, A., Jackson, P., and Inman-Bamber, G. (2021). Sugarcane. Crop Physiology Case Histories for Major Crops, Elsevier.","DOI":"10.1016\/B978-0-12-819194-1.00021-9"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2877","DOI":"10.1093\/jxb\/erq144","article-title":"Biomass accumulation in sugarcane: Unravelling the factors underpinning reduced growth phenomena","volume":"61","author":"Donaldson","year":"2010","journal-title":"J. Exp. Bot."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"4373","DOI":"10.1093\/jxb\/erab118","article-title":"Predicting photosynthetic capacity in tobacco using shortwave infrared spectral reflectance","volume":"72","author":"Sexton","year":"2021","journal-title":"J. Exp. Bot."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"6094","DOI":"10.1080\/01431161.2013.793860","article-title":"Field spectroscopy for weed detection in wheat and chickpea fields","volume":"34","author":"Shapira","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_63","unstructured":"Neale, C.M.U., Owe, M., and D\u2019Urso, G. (2008, January 15\u201318). Imaging spectroscopy for estimating sugarcane leaf nitrogen concentration. Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology X, Wales, UK."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/bs.agron.2020.06.001","article-title":"Near infrared (NIR) spectroscopy as a rapid and cost-effective method for nutrient analysis of plant leaf tissues","volume":"164","author":"Prananto","year":"2020","journal-title":"Adv. Agron."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4250\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:32:36Z","timestamp":1760113956000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4250"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,14]]},"references-count":64,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["rs16224250"],"URL":"https:\/\/doi.org\/10.3390\/rs16224250","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,14]]}}}