{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T15:08:14Z","timestamp":1768835294170,"version":"3.49.0"},"reference-count":64,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2016,6,14]],"date-time":"2016-06-14T00:00:00Z","timestamp":1465862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sugarcane is an important economic resource for many tropical countries and optimizing plantations is a serious concern with economic and environmental benefits. One of the best ways to optimize the use of resources in those plantations is to minimize the occurrence of gaps. Typically, gaps open in the crop canopy because of damaged rhizomes, unsuccessful sprouting or death young stalks. In order to avoid severe yield decrease, farmers need to fill the gaps with new plants. Mapping gap density is therefore critical to evaluate crop planting quality and guide replanting. Current field practices of linear gap evaluation are very labor intensive and cannot be performed with sufficient intensity as to provide detailed spatial information for mapping, which makes replanting difficult to perform. Others have used sensors carried by land vehicles to detect gaps, but these are complex and require circulating over the entire area. We present a method based on processing digital mosaics of conventional images acquired from a small Unmanned Aerial Vehicle  (UAV) that produced a map of gaps at 23.5 cm resolution in a study area of 8.7 ha with 92.9% overall accuracy. Linear Gap percentage estimated from this map for a grid with cells of 10 m \u00d7 10 m linearly correlates with photo-interpreted linear gap percentage with a coefficient of determination (R2)= 0.9; a root mean square error (RMSE) = 5.04; and probability (p) &lt;&lt; 0.01. Crop Planting Quality levels calculated from image-derived gaps agree with those calculated from a photo-interpreted version of currently used field methods (Spearman coefficient = 0.92). These results clearly demonstrate the effectiveness of processing mosaics of Unmanned Aerial System (UAS) images for mapping gap density and, together with previous studies using satellite and hand-held spectroradiometry, suggests the extension towards multi-spectral imagery to add insight on plant condition.<\/jats:p>","DOI":"10.3390\/rs8060500","type":"journal-article","created":{"date-parts":[[2016,6,14]],"date-time":"2016-06-14T11:12:12Z","timestamp":1465902732000},"page":"500","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Mapping Crop Planting Quality in Sugarcane from UAV Imagery: A Pilot Study in Nicaragua"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2212-9767","authenticated-orcid":false,"given":"Inti","family":"Luna","sequence":"first","affiliation":[{"name":"Evolo Company, Reparto San Juan 142-A, Managua, Nicaragua"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6689-2908","authenticated-orcid":false,"given":"Agust\u00edn","family":"Lobo","sequence":"additional","affiliation":[{"name":"Instituto de Ciencias de la Tierra \u201cJaume Almera\u201d (CSIC), Lluis Sol\u00e9 Sabar\u00eds s\/n, 08028 Barcelona, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2016,6,14]]},"reference":[{"key":"ref_1","unstructured":"FAO (2009). Handbook of Sugar Beet, FAO. Agribussines."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1016\/j.biombioe.2007.12.006","article-title":"Green house gases emissions in the production and use of ethanol from sugarcane in Brazil: The 2005\/2006 averages and a prediction for 2020","volume":"32","author":"Macedo","year":"2008","journal-title":"Biomass Bioenergy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1007\/BF02943642","article-title":"Potential of developing sugarcane by-product based industries in India","volume":"8","author":"Yadav","year":"2006","journal-title":"Sugar Tech"},{"key":"ref_4","unstructured":"FAOSTAT. Available online: http:\/\/faostat3.fao.org\/download\/Q\/QC\/E."},{"key":"ref_5","unstructured":"CNPA (2015). Production Report for Harvest 2014\u20132015, Comision Nacional Productores de Azucar."},{"key":"ref_6","unstructured":"INIDE (2012). Censo Nacional Agropecuario, INIDE."},{"key":"ref_7","unstructured":"Fischer, G., Teixeira, E., Tothne, E., and van Velthuizen, H. (2008). Sugarcane Ethanol: Contribution to Climate Change Mitigation and the Environment, Wageningen Academic Publisher."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1890\/07-1813.1","article-title":"Expansion of sugarcane ethanol production in Brazil: Environmental and social challenges","volume":"18","author":"Martinelli","year":"2008","journal-title":"Ecol. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2086","DOI":"10.1016\/j.enpol.2008.02.028","article-title":"The sustainability of ethanol production from sugarcane","volume":"36","author":"Goldemberg","year":"2008","journal-title":"Energy Policy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1177\/0270467609333728","article-title":"The ecological impacts of large-scale agrofuel monoculture production systems in the Americas","volume":"29","author":"Altieri","year":"2009","journal-title":"Bull. Sci. Technol. Soc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1038\/nature10452","article-title":"Solutions for a cultivated planet","volume":"478","author":"Foley","year":"2011","journal-title":"Nature"},{"key":"ref_12","unstructured":"CNPA (2016). Production Report for Harvest 2015\u20132016. First Estimate, Comisi\u00f3n Nacional Productores de Az\u00facar."},{"key":"ref_13","unstructured":"Santos, F., Bor\u00e9m, A., and Caldas, C. (2015). Sugarcane: Agricultural Production, Bioenergy and Ethanol, Academic Press."},{"key":"ref_14","first-page":"11","article-title":"Water-sugarcane relationships","volume":"48","author":"Gascho","year":"1985","journal-title":"Sugar J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.jterra.2004.10.010","article-title":"Agricultural traffic impacts on soil","volume":"42","author":"Raper","year":"2005","journal-title":"J. Terramech."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"161","DOI":"10.13031\/2013.42642","article-title":"Assessing damage caused by accidental vehicle traffic on sugarcane ratoon","volume":"29","author":"Paula","year":"2013","journal-title":"Appl. Eng. Agric."},{"key":"ref_17","first-page":"12","article-title":"Methodology for gap evaluation on sugarcane lines","volume":"4","author":"Stolf","year":"1986","journal-title":"STAB Piracicaba"},{"key":"ref_18","unstructured":"Alvares, C.A., de Oliveira, C.F., Valad\u00e3o, F.T., Molin, J.P., Salvi, J.V., and Fortes, C. (2008). Remote Sensing for Mapping Sugarcane Failures, Congresso Brasileiro de Agricultura de Precisao."},{"key":"ref_19","unstructured":"Molin, J.P., Veiga, J.P.S., and Cavalcante, D.S.C. (2014). Measuring and Mapping Sugarcane Gaps, University of S\u00e3o Paulo."},{"key":"ref_20","first-page":"6","article-title":"On-farm profitability of remote sensing in agriculture","volume":"1","author":"Tenkorang","year":"2008","journal-title":"J. Terr. Obs."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"949","DOI":"10.3390\/rs5020949","article-title":"Advances in Remote Sensing of agriculture: Context description, existing operational monitoring systems and major information needs","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S., Lyon, J.G., and Huete, A. (2011). Hyperspectral. Remote Sensing of Vegetation, CRC Press.","DOI":"10.1201\/b11222-41"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"043560","DOI":"10.1117\/1.3525241","article-title":"Estimation of leaf nitrogen and silicon using hyperspectral remote sensing","volume":"4","author":"Mokhele","year":"2010","journal-title":"J. Appl. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5006","DOI":"10.3390\/rs5105006","article-title":"Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture","volume":"5","author":"Honkavaara","year":"2013","journal-title":"Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3753","DOI":"10.1080\/01431160701874603","article-title":"The application of remote sensing techniques to sugarcane (Saccharum spp. hybrid) production: A review of the literature","volume":"29","author":"Ahmed","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","unstructured":"Tulip, J.R., and Wilkins, K. Application of spectral unmixing to trash level estimation in billet cane. Proceedings of the 2005 Conference of the Australian Society of Sugar Cane Technologists."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1590\/S0103-90162005000300001","article-title":"Spectral variables, growth analysis and yield of sugarcane","volume":"62","author":"Dos","year":"2005","journal-title":"Sci. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1651","DOI":"10.3390\/rs4061651","article-title":"Estimating canopy nitrogen concentration in sugarcane using field imaging spectroscopy","volume":"4","author":"Miphokasap","year":"2012","journal-title":"Remote Sens."},{"key":"ref_29","unstructured":"Schmidt, E.J., Narciso, G., Frost, P., and Gers, C. (2000, January 1\u20133). Application of remote sensing technology in the SA Sugar Industry\u2013A review of recent research findings. Proceedings of the 74th Annual Congress of the South African Sugar Technologists\u2019 Association, Durban, South Africa."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s11119-012-9274-5","article-title":"The application of small unmanned aerial systems for precision agriculture: A review","volume":"13","author":"Zhang","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Stafford, J.V. (2013). Precision Agriculture \u201913, Wageningen Academic Publishers.","DOI":"10.3920\/978-90-8686-778-3"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1007\/s11119-012-9257-6","article-title":"A flexible unmanned aerial vehicle for precision agriculture","volume":"13","author":"Primicerio","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zarco-Tejada, P.J. (2008). A new era in remote sensing of crops with unmanned robots. SPIE Newsroom.","DOI":"10.1117\/2.1200812.1438"},{"key":"ref_34","unstructured":"INETER. Available online: http:\/\/servmet.ineter.gob.ni\/Meteorologia\/climadenicaragua.php."},{"key":"ref_35","unstructured":"INETER (1975). Taxonomia de Suelos de Nicaragua, Publisher INETER."},{"key":"ref_36","unstructured":"Quantum GIS Development Team Quantum GIS Geographic Information System, Open Source Geospatial Foundation 2009. Available online: http:\/\/qgis.osgeo.org."},{"key":"ref_37","unstructured":"R Core Team (2012). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_38","unstructured":"Keitt, T.H., Bivand, R., Pebesma, E., and Rowlingson, B. Available online: https:\/\/cran.r-project.org\/web\/packages\/rgdal\/index.html."},{"key":"ref_39","unstructured":"Hijmans, R.J., and van Etten, J. Raster: Geographic Analysis and Modeling with Raster Data, R Package Version, 2012. Available online: https:\/\/cran.r-project.org\/web\/packages\/raster\/index.html."},{"key":"ref_40","unstructured":"Bivand, R., and Rundel, C. Rgeos: Interface to Geometry Engine-Open Source (GEOS), R Package Version, 2015. Available online: https:\/\/cran.r-project.org\/web\/packages\/rgeos\/index.html."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Venables, W.N., and Ripley, B.D. (2002). Modern. Applied Statistics with S, Springer. [4th ed.].","DOI":"10.1007\/978-0-387-21706-2"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wickham, H. (2009). Ggplot2: Elegant Graphics for Data Analysis, Springer.","DOI":"10.1007\/978-0-387-98141-3"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"136992","DOI":"10.18637\/jss.v040.i01","article-title":"The split-apply-combine strategy for data analysis","volume":"40","author":"Wickham","year":"2011","journal-title":"J. Statist. Softw."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Richards, J.A., and Jia, X. (2005). Remote Sensing Digital Image Analysis: An Introduction, Springer Verlag. [4th ed.].","DOI":"10.1007\/3-540-29711-1"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1136","DOI":"10.1109\/36.628781","article-title":"Image segmentation and discriminant analysis for the identification of land cover units in ecology","volume":"35","author":"Lobo","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2529","DOI":"10.3390\/rs3112529","article-title":"Multispectral Remote Sensing from unmanned aircraft: Image processing workflows and applications for rangeland environments","volume":"3","author":"Laliberte","year":"2011","journal-title":"Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Huang, J., Wang, J., Zhang, K., Kuang, Z., Zhong, S., and Song, X. (2015). Object-oriented classification of sugarcane using time-series middle-resolution Remote Sensing data based on adaboost. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0142069"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"289","DOI":"10.5721\/EuJRS20134616","article-title":"Mapping invasive woody plants in Azores Protected Areas by using very high-resolution multispectral imagery","volume":"46","author":"Gil","year":"2013","journal-title":"Eur. J. Remote Sens."},{"key":"ref_51","unstructured":"Heckbert, P.S. (1994). Graphics Gems IV, Academic Press Professional, Inc."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"725","DOI":"10.3390\/rs70100725","article-title":"Angular dependency of hyperspectral measurements over wheat characterized by a novel UAV based goniometer","volume":"7","author":"Burkart","year":"2015","journal-title":"Remote Sens."},{"key":"ref_53","unstructured":"Pe\u00f1a-Barrag\u00e1n, J.M., Kelly, M., de-Castro, A.I., and L\u00f3pez-Granados, F. (2012, January 7\u20139). Object-based approach for crop row characterization in uav images for site-specific weed management. Proceedings of the 4th GEOBIA, Rio de Janeiro, Brazil."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2380","DOI":"10.1016\/j.rse.2009.06.018","article-title":"Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery","volume":"113","author":"Berni","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_55","first-page":"281","article-title":"Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV)","volume":"171\u2013172","author":"Catalina","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.geomorph.2012.08.021","article-title":"\u201cStructure-from-Motion\u201d photogrammetry: A low-cost, effective tool for geoscience applications","volume":"179","author":"Westoby","year":"2012","journal-title":"Geomorphology"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"6880","DOI":"10.3390\/rs5126880","article-title":"Using unmanned aerial vehicles (UAV) for high-resolution reconstruction of topography: The structure from motion approach on coastal environments","volume":"5","author":"Mancini","year":"2013","journal-title":"Remote Sens."},{"key":"ref_58","unstructured":"Sullivan, D., and Brown, A. (2002, January 28\u201330). High accuracy autonomous image georeferencing using a GPS\/Inertial-aided digital imaging system. Proceedings of the 2002 National Technical Meeting of The Institute of Navigation, San Diego, CA, USA."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"380","DOI":"10.3390\/agronomy4030380","article-title":"Scaling of thermal images at different spatial resolution: The mixed pixel problem","volume":"4","author":"Jones","year":"2014","journal-title":"Agronomy"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s11119-013-9334-5","article-title":"Mapping crop water stress index in a \u201cPinot-noir\u201d vineyard: Comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle","volume":"15","author":"Bellvert","year":"2014","journal-title":"Precis. Agric."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.eja.2014.01.004","article-title":"Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods","volume":"55","author":"Angileri","year":"2014","journal-title":"Eur. J. Agron."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"45","DOI":"10.5194\/isprsarchives-XL-1-W2-45-2013","article-title":"Very high resolution crop surface models (CSMs) from UAV-based stereo images for rice growth monitoring in Northeast China","volume":"40","author":"Bendig","year":"2013","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"10335","DOI":"10.3390\/rs61110335","article-title":"Combined spectral and spatial modeling of corn yield based on aerial images and crop surface models acquired with an unmanned aircraft system","volume":"6","author":"Geipel","year":"2014","journal-title":"Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"5584","DOI":"10.3390\/rs70505584","article-title":"Early detection and quantification of verticillium wilt in olive using hyperspectral and thermal imagery over large areas","volume":"7","year":"2015","journal-title":"Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/6\/500\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:25:27Z","timestamp":1760210727000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/6\/500"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,6,14]]},"references-count":64,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2016,6]]}},"alternative-id":["rs8060500"],"URL":"https:\/\/doi.org\/10.3390\/rs8060500","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,6,14]]}}}