{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T07:21:53Z","timestamp":1775546513446,"version":"3.50.1"},"reference-count":76,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,25]],"date-time":"2022-02-25T00:00:00Z","timestamp":1645747200000},"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>The use of satellite-based Remote Sensing (RS) is a well-developed field of research. RS techniques have been successfully utilized to evaluate the chlorophyll content for the monitoring of sugarcane crops. This research provides a new framework for inferring the chlorophyll content in sugarcane crops at the canopy level using unmanned aerial vehicles (UAVs) and spectral vegetation indices processed with multiple machine learning algorithms. Studies were conducted in a sugarcane field located in Sugarcane Research Institute (SRI, Uda Walawe, Sri Lanka), with various fertilizer applications over the entire growing season from 2020 to 2021. An UAV with multispectral camera was used to collect the aerial images to generate the vegetation indices. Ground measurements of leaf chlorophyll were used as indications for fertilizer status in the sugarcane field. Different machine learning (ML) algorithms were used ground-truthing data of chlorophyll content and spectral vegetation indices to forecast sugarcane chlorophyll content. Several machine learning algorithms such as MLR, RF, DT, SVR, XGB, KNN and ANN were applied in two ways: before feature selection (BFS) by training the algorithms with all twenty-four (24) vegetation indices with five (05) spectral bands and after feature selection (AFS) by training algorithms with fifteen (15) vegetation indices. All the algorithms with both BFS and AFS methods were compared with an estimated coefficient of determination (R2) and root mean square error (RMSE). Spectral indices such as RVI and DVI were shown to be the most reliable indices for estimating chlorophyll content in sugarcane fields, with coefficients of determination (R2) of 0.94 and 0.93, respectively. XGB model shows the highest validation score (R2) and lowest RMSE in both methods of BFS (0.96 and 0.14) and AFS (0.98 and 0.78), respectively. However, KNN and SVR algorithms show the lowest validation accuracy than other models. According to the results, the AFS validation score is higher than BFS in MLR, SVR, XGB and KNN. Even though, validation score of the ANN model is decreased in AFS. The findings demonstrated that the use of multispectral UAV could be utilized to estimate chlorophyll content and measure crop health status over a larger sugarcane field. This methodology will aid in real-time crop nutrition management in sugarcane plantations by reducing the need for conventional measurement of sugarcane chlorophyll content.<\/jats:p>","DOI":"10.3390\/rs14051140","type":"journal-article","created":{"date-parts":[[2022,2,27]],"date-time":"2022-02-27T20:48:33Z","timestamp":1645994913000},"page":"1140","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":106,"title":["Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7793-2680","authenticated-orcid":false,"given":"Amarasingam","family":"Narmilan","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Robotics, Faculty of Engineering, Queensland University of Technology (QUT), 2 George Street, Brisbane, QLD 4000, Australia"},{"name":"Department of Biosystems Technology, Faculty of Technology, South Eastern University of Sri Lanka, University Park, Oluvil 32360, Sri Lanka"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4342-3682","authenticated-orcid":false,"given":"Felipe","family":"Gonzalez","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Robotics, Faculty of Engineering, Queensland University of Technology (QUT), 2 George Street, Brisbane, QLD 4000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9962-9508","authenticated-orcid":false,"given":"Arachchige Surantha Ashan","family":"Salgadoe","sequence":"additional","affiliation":[{"name":"Department of Horticulture and Landscape Gardening, Wayamba University of Sri Lanka, Makandura, Gonawila 60170, Sri Lanka"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8066-8411","authenticated-orcid":false,"given":"Unupen Widanelage Lahiru Madhushanka","family":"Kumarasiri","sequence":"additional","affiliation":[{"name":"Division of Crop Nutrition, Sugarcane Research Institute, Dakunu Ela Road, Udawalawe 70190, Sri Lanka"}]},{"given":"Hettiarachchige Asiri Sampageeth","family":"Weerasinghe","sequence":"additional","affiliation":[{"name":"Division of Crop Nutrition, Sugarcane Research Institute, Dakunu Ela Road, Udawalawe 70190, Sri Lanka"}]},{"given":"Buddhika Rasanjana","family":"Kulasekara","sequence":"additional","affiliation":[{"name":"Division of Crop Nutrition, Sugarcane Research Institute, Dakunu Ela Road, Udawalawe 70190, Sri Lanka"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Vanegas, F., Bratanov, D., Powell, K., Weiss, J., and Gonzalez, F. (2018). A Novel Methodology for Improving Plant Pest Surveillance in Vineyards and Crops Using UAV-Based Hyperspectral and Spatial Data. Sensors, 18.","DOI":"10.3390\/s18010260"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Thomas, J.E., Wood, T.A., Gullino, M.L., Ortu, G., Thomas, J.E., and Wood, T.A. (2017). Diagnostic Tools for Plant Biosecurity. Practical Tools for Plant and Food Biosecurity, Springer.","DOI":"10.1007\/978-3-319-46897-6_10"},{"key":"ref_3","unstructured":"Mcfadyen, A., Gonzalez, L.F., Campbell, D.A., and Eagling, D. (2014). Evaluating Unmanned Aircraft Systems for Deployment in Plant Biosecurity, Queensland University of Technology."},{"key":"ref_4","unstructured":"Puig Garcia, E., Gonzalez, F., Hamilton, G., and Grundy, P. (December, January 29). Assessment of crop insect damage using unmanned aerial systems: A machine learning approach. Proceedings of the MODSIM 2015, 21st International Congress on Modelling and Simulation, Gold Coast, Australia. Available online: http:\/\/www.mssanz.org.au\/modsim2015\/F12\/puig.pdf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.pbi.2020.04.011","article-title":"Blurred lines: Integrating emerging technologies to advance plant biosecurity","volume":"56","author":"Hu","year":"2020","journal-title":"Curr. Opin. Plant Biol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sandino, J., Pegg, G., Gonzalez, F., and Smith, G. (2018). Aerial Mapping of Forests Affected by Pathogens Using UAVs, Hyperspectral Sensors, and Artificial Intelligence. Sensors, 18.","DOI":"10.3390\/s18040944"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"105909","DOI":"10.1016\/j.compag.2020.105909","article-title":"State and parameter estimation of the AquaCrop model for winter wheat using sensitivity informed particle filter","volume":"180","author":"Zhang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105898","DOI":"10.1016\/j.compag.2020.105898","article-title":"Utility of a commercial unmanned aerial vehicle for in-field localization of biomass bales","volume":"180","author":"Seyyedhasani","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","first-page":"1193","article-title":"A light-weight multispectral sensor for micro UAV\u2014Opportunities for very high resolution airborne remote sensing","volume":"37","author":"Nebiker","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat.-Form. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1080\/10798587.2008.10643309","article-title":"The Application of Unmanned Aerial Vehicle Remote Sensing in Quickly Monitoring Crop Pests","volume":"18","author":"Yue","year":"2012","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Aasen, H., Honkavaara, E., Lucieer, A., and Zarco-Tejada, P.J. (2018). Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows. Remote Sens., 10.","DOI":"10.3390\/rs10071091"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s40677-017-0073-1","article-title":"Spaceborne, UAV and ground-based remote sensing techniques for landslide mapping, monitoring and early warning","volume":"4","author":"Casagli","year":"2017","journal-title":"Geoenviron. Disasters"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.biosystemseng.2010.11.010","article-title":"Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV)","volume":"108","author":"Xiang","year":"2011","journal-title":"Biosyst. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/S0034-4257(03)00131-7","article-title":"Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression","volume":"86","author":"Hansen","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/S0034-4257(02)00018-4","article-title":"Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture","volume":"81","author":"Haboudane","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hoeppner, J.M., Skidmore, A.K., Darvishzadeh, R., Heurich, M., Chang, H.-C., and Gara, T.W. (2020). Mapping Canopy Chlorophyll Content in a Temperate Forest Using Airborne Hyperspectral Data. Remote Sens., 12.","DOI":"10.3390\/rs12213573"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Shah, S.H., Angel, Y., Houborg, R., Ali, S., and McCabe, M.F. (2019). A Random Forest Machine Learning Approach for the Retrieval of Leaf Chlorophyll Content in Wheat. Remote Sens., 11.","DOI":"10.3390\/rs11080920"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106174","DOI":"10.1016\/j.compag.2021.106174","article-title":"Evaluating the sensitivity of water stressed maize chlorophyll and structure based on UAV derived vegetation indices","volume":"185","author":"Zhang","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","first-page":"24","article-title":"Real time estimation of chlorophyll content based on vegetation indices derived from multispectral UAV in the kinnow orchard","volume":"1","author":"Tahir","year":"2018","journal-title":"Int. J. Precis. Agric. Aviat."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.3390\/f5061481","article-title":"Small Drones for Community-Based Forest Monitoring: An Assessment of Their Feasibility and Potential in Tropical Areas","volume":"5","author":"McCall","year":"2014","journal-title":"Forests"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Jang, G., Kim, J., Yu, J.-K., Kim, H.-J., Kim, Y., Kim, D.-W., Kim, K.-H., Lee, C.W., and Chung, Y.S. (2020). Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application. Remote Sens., 12.","DOI":"10.3390\/rs12060998"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Themistocleous, K. (2014, January 7\u201310). The use of UAV platforms for remote sensing applications: Case studies in Cyprus. Proceedings of the Second International Conference on Remote Sensing and Geoinformation of Environment, Pafos, Cyprus.","DOI":"10.1117\/12.2069514"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.3389\/fpls.2017.01111","article-title":"Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives","volume":"8","author":"Yang","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Custers, B. (2016). Drone Technology: Types, Payloads, Applications, Frequency Spectrum Issues and Future Developments. The Future of Drone Use, TMC Asser Press.","DOI":"10.1007\/978-94-6265-132-6"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Delavarpour, N., Koparan, C., Nowatzki, J., Bajwa, S., and Sun, X. (2021). A Technical Study on UAV Characteristics for Precision Agriculture Applications and Associated Practical Challenges. Remote Sens., 13.","DOI":"10.3390\/rs13061204"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Miphokasap, P., and Wannasiri, W. (2018). Estimations of Nitrogen Concentration in Sugarcane Using Hyperspectral Imagery. Sustainability, 10.","DOI":"10.3390\/su10041266"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1016\/j.agrformet.2008.12.007","article-title":"Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices","volume":"149","author":"Wu","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5625","DOI":"10.1093\/jxb\/erv270","article-title":"Comparing vegetation indices for remote chlorophyll measurement of white poplar and Chinese elm leaves with different adaxial and abaxial surfaces","volume":"66","author":"Lu","year":"2015","journal-title":"J. Exp. Bot."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Fawcett, D., Panigada, C., Tagliabue, G., Boschetti, M., Celesti, M., Evdokimov, A., Biriukova, K., Colombo, R., Miglietta, F., and Rascher, U. (2020). Multi-Scale Evaluation of Drone-Based Multispectral Surface Reflectance and Vegetation Indices in Operational Conditions. Remote Sens., 12.","DOI":"10.3390\/rs12030514"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Osco, L.P., Ramos, A.P.M., Pereira, D.R., Moriya, A.S., Imai, N.N., Matsubara, E.T., Estrabis, N., De Souza, M., Junior, J.M., and Gon\u00e7alves, W.N. (2019). Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11242925"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cui, B., Zhao, Q., Huang, W., Song, X., Ye, H., and Zhou, X. (2019). A New Integrated Vegetation Index for the Estimation of Winter Wheat Leaf Chlorophyll Content. Remote. Sens., 11.","DOI":"10.3390\/rs11080974"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"L08403","DOI":"10.1029\/2005GL022688","article-title":"Remote estimation of canopy chlorophyll content in crops","volume":"32","author":"Gitelson","year":"2005","journal-title":"Geophys. Res. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ballester, C., Brinkhoff, J., Quayle, W.C., and Hornbuckle, J. (2019). Monitoring the Effects of Water Stress in Cotton using the Green Red Vegetation Index and Red Edge Ratio. Remote Sens., 11.","DOI":"10.3390\/rs11070873"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.1016\/j.rse.2010.04.006","article-title":"New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat","volume":"114","author":"Chen","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_35","first-page":"105880","article-title":"Predicting pasture biomass using a statistical model and machine learning algorithm implemented with remotely sensed imagery","volume":"180","author":"Basso","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Feng, L., Zhang, Z., Ma, Y., Du, Q., Williams, P., Drewry, J., and Luck, B. (2020). Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning. Remote Sens., 12.","DOI":"10.3390\/rs12122028"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhou, X., Yang, L., Wang, W., and Chen, B. (2021). UAV Data as an Alternative to Field Sampling to Monitor Vineyards Using Machine Learning Based on UAV\/Sentinel-2 Data Fusion. Remote Sens., 13.","DOI":"10.3390\/rs13030457"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.geoderma.2019.05.031","article-title":"Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review","volume":"352","author":"Lamichhane","year":"2019","journal-title":"Geoderma"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Fu, Y., Yang, G., Song, X., Li, Z., Xu, X., Feng, H., and Zhao, C. (2021). Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis. Remote Sens., 13.","DOI":"10.3390\/rs13040581"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s13007-019-0394-z","article-title":"Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data","volume":"15","author":"Han","year":"2019","journal-title":"Plant Methods"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1093\/treephys\/20.16.1113","article-title":"Differentiation among effects of nitrogen fertilization treatments onconifer seedlings by foliar reflectance: A comparison of method","volume":"20","author":"Moran","year":"2000","journal-title":"Tree Physiol."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xu, J.-X., Ma, J., Tang, Y.-N., Wu, W.-X., Shao, J.-H., Wu, W.-B., Wei, S.-Y., Liu, Y.-F., Wang, Y.-C., and Guo, H.-Q. (2020). Estimation of Sugarcane Yield Using a Machine Learning Approach Based on UAV-LiDAR Data. Remote Sens., 12.","DOI":"10.3390\/rs12172823"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Canata, T., Wei, M., Maldaner, L., and Molin, J. (2021). Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique. Remote Sens., 13.","DOI":"10.3390\/rs13020232"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Lee, H., Wang, J., and Leblon, B. (2020). Using Linear Regression, Random Forests, and Support Vector Machine with Unmanned Aerial Vehicle Multispectral Images to Predict Canopy Nitrogen Weight in Corn. Remote Sens., 12.","DOI":"10.3390\/rs12132071"},{"key":"ref_45","unstructured":"(2022, January 21). QGIS.org. QGIS Geographic Information System. QGIS Association. Available online: http:\/\/www.qgis.org."},{"key":"ref_46","first-page":"97","article-title":"Narrow band based and broadband derived vegetation indices using Sentinel-2 Imagery to estimate vegetation biomass","volume":"6","author":"Imran","year":"2020","journal-title":"Glob. J. Environ. Sci. Manag."},{"key":"ref_47","first-page":"420","article-title":"Estimation of vegetation fraction using RGB and multispectral images from UAV","volume":"40","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Avola, G., Di Gennaro, S.F., Cantini, C., Riggi, E., Muratore, F., Tornamb\u00e8, C., and Matese, A. (2019). Remotely Sensed Vegetation Indices to Discriminate Field-Grown Olive Cultivars. Remote Sens., 11.","DOI":"10.3390\/rs11101242"},{"key":"ref_49","first-page":"20","article-title":"Comparison of NDVI and NDRE Indices to Detect Differences in Vegetation and Chlorophyll Content","volume":"4","author":"Boiarskii","year":"2019","journal-title":"J. Mech. Contin. Math. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3640","DOI":"10.1016\/j.rse.2011.09.002","article-title":"Broadband, red-edge information from satellites improves early stress detection in a New Mexico conifer woodland","volume":"115","author":"Eitel","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wang, C., Yang, C., Xie, T., Jiang, Z., Hu, T., Luo, Z., Zhou, G., and Xie, J. (2020). Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring. Remote Sens., 12.","DOI":"10.3390\/rs12071207"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"119493","DOI":"10.1016\/j.foreco.2021.119493","article-title":"Early detection of pine wilt disease using deep learning algorithms and UAV-based multispectral imagery","volume":"497","author":"Yu","year":"2021","journal-title":"For. Ecol. Manag."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_54","first-page":"70","article-title":"Comparison of different reflectance indices for vegetation analysis using Landsat-TM data","volume":"12","author":"Kumar","year":"2018","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1029\/2002GL016450","article-title":"Remote estimation of leaf area index and green leaf biomass in maize canopies","volume":"30","author":"Gitelson","year":"2003","journal-title":"Geophys. Res. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.3390\/rs6021211","article-title":"The Generalized Difference Vegetation Index (GDVI) for Dryland Characterization","volume":"6","author":"Wu","year":"2014","journal-title":"Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"e11401","DOI":"10.1002\/aps3.11401","article-title":"Application of remote sensing technology to estimate productivity and assess phylogenetic heritability","volume":"8","author":"Scher","year":"2020","journal-title":"Appl. Plant Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.biosystemseng.2014.11.007","article-title":"Monitoring of crop biomass using true colour aerial photographs taken from a remote controlled hexacopter","volume":"129","author":"Jannoura","year":"2015","journal-title":"Biosyst. Eng."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1353691","DOI":"10.1155\/2017\/1353691","article-title":"Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"012001","DOI":"10.1088\/1755-1315\/149\/1\/012001","article-title":"Selection of vegetation indices for mapping the sugarcane condition around the oil and gas field of North West Java Basin, Indonesia","volume":"149","author":"Susantoro","year":"2018","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Capolupo, A., Monterisi, C., and Tarantino, E. (2020). Landsat Images Classification Algorithm (LICA) to Automatically Extract Land Cover Information in Google Earth Engine Environment. Remote Sens., 12.","DOI":"10.3390\/rs12071201"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.compag.2018.10.006","article-title":"Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images","volume":"155","author":"Kerkech","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s41324-020-00339-5","article-title":"Evaluation of the saturation property of vegetation indices derived from sentinel-2 in mixed crop-forest ecosystem","volume":"29","author":"Tesfaye","year":"2021","journal-title":"Spat. Inf. Res."},{"key":"ref_64","unstructured":"Melillos, G., and Hadjimitsis, D.G. (May, January 27). Using simple ratio (SR) vegetation index to detect deep man-made infrastructures in Cyprus. Proceedings of the Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXV, Online."},{"key":"ref_65","unstructured":"Salisu, A., Abubakar, H., and Abubakar, H. (2018, January 27). One Way Anova: Concepts and Application in Agricultural System. Proceedings of the CEUR Workshop Proceedings, Kaunas, Lithuania."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/978-3-540-93905-4_31","article-title":"Comparison of Various Feature Selection Methods in Application to Prototype Best Rules","volume":"57","author":"Blachnik","year":"2009","journal-title":"Adv. Intell. Soft Comput."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"105791","DOI":"10.1016\/j.compag.2020.105791","article-title":"A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices","volume":"178","author":"Ramos","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.enbuild.2012.11.010","article-title":"Multiple regression model for fast prediction of the heating energy demand","volume":"57","author":"Catalina","year":"2013","journal-title":"Energy Build."},{"key":"ref_69","unstructured":"Perlich, C., Provost, F., and Simonoff, J.S. (2003). Tree Induction vs. Logistic Regression: A Learning-Curve Analysis. J. Mach. Learn. Res., 4."},{"key":"ref_70","first-page":"397","article-title":"The Learning-Curve Sampling Method Applied to Model-Based Clustering","volume":"2","author":"Meek","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Jozdani, S.E., Johnson, B.A., and Chen, D. (2019). Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use\/Land Cover Classification. Remote Sens., 11.","DOI":"10.3390\/rs11141713"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"168137","DOI":"10.1109\/ACCESS.2020.3023690","article-title":"Transparency Estimation of Narrow Rivers by UAV-Borne Hyperspectral Remote Sensing Imagery","volume":"8","author":"Wei","year":"2020","journal-title":"IEEE Access"},{"key":"ref_73","first-page":"102618","article-title":"Winter wheat SPAD estimation from UAV hyperspectral data using cluster-regression methods","volume":"105","author":"Yang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_74","first-page":"2169","article-title":"Application of Machine Learning Algo-rithms in Plant Breeding: Predicting Yield from Hyperspectral Reflectance in Soybean","volume":"11","author":"Earl","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Dong, T., Shang, J., Chen, J.M., Liu, J., Qian, B., Ma, B., Morrison, M.J., Zhang, C., Liu, Y., and Shi, Y. (2019). Assessment of Portable Chlorophyll Meters for Measuring Crop Leaf Chlorophyll Concentration. Remote Sens., 11.","DOI":"10.3390\/rs11222706"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"105026","DOI":"10.1016\/j.compag.2019.105026","article-title":"Estimating biomass of winter oilseed rape using vegetation indices and texture metrics derived from UAV multispectral images","volume":"166","author":"Liu","year":"2019","journal-title":"Comput. Electron. Agric."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1140\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:27:31Z","timestamp":1760135251000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1140"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,25]]},"references-count":76,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14051140"],"URL":"https:\/\/doi.org\/10.3390\/rs14051140","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,25]]}}}