[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128105-en":3,"doc-seo-128105-105":31,"detail-sidebar-cat-0-en-105":84},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128105,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","NDVI Prediction with RGB UAV Imagery Utilizing Advanced Machine Learning Regression Models","UAV imaging technologies increasingly shape sensor capabilities and broaden research and practical UAV applications. This study investigates low-cost RGB UAV imagery for agricultural monitoring by predicting NDVI, leveraging a multispectral UAV NDVI map as reference. The prediction pipeline evaluates CatBoost, LightGBM, and a stacking ensemble across pixel-level outputs in an urban area with dense vegetation. Model quality is assessed using R², RMSE, NMAD, and STD, showing broadly similar performance with slightly lower accuracy for LightGBM.","NDVI Prediction with RGB UAV Imagery Utilizing Advanced Machine Learning Regression  \nModels  \nIlyas Aydin 1, Umut Gunes Sefercik 1  \n1 GTU, Engineering Faculty, Dept. of Geomatics Engineering 41400, Kocaeli, [Turkey-ilyasaydin@gtu.edu.tr](Turkey-ilyasaydin@gtu.edu.tr), [sefercik@gtu.edu.tr](sefercik@gtu.edu.tr)  \nKeywords: NDVI prediction, Multispectral UAV, RGB UAV, CatBoost, LightGBM, Stacking Ensemble model.  \nAbstract  \nThe ever-evolving technology has significantly affected the sensors used in UAV cameras and has played an important role in the expansion of the application areas of hobbyist and commercial UAVs. In particular, UAVs with multispectral (MS) cameras, which have the potential to detect a wide range of spectral information, are widely used in many popular research areas such as precision agriculture and forestry. However, despite their advanced capabilities, the high cost of these technologies limits their accessibility for basic users. In this study, the agricultural potential of RGB UAVs, which have a much wider user base due to their lower cost, was investigated by predicting the Normalized Difference Vegetation Index (NDVI), which is widely preferred for plant classification, growth and health monitoring. In the literature, RGB camera-based NDVI prediction studies involving machine learning and deep learning algorithms have focused on the correlation of the results with the reference data (R²) or the model accuracy of the algorithms used. The approaches applied have generally been tested on single photographs or solely on vegetation areas. In this study, using the MS UAV NDVI map as reference, a comprehensive evaluation approach was applied where each pixel of the NDVI prediction maps produced by categorical boosting (CatBoost), light gradient boosting machine (LightGBM) and a stacking ensemble learning model obtained from the combination of both algorithms, whose performance in NDVI estimation has not been tested extensively before. The models were tested in an urban area with numerous buildings and a large study area with dense vegetation. The performance of the NDVI maps was analyzed using R², Root Mean Square Error (RMSE), Normalized Median Absolute Deviation (NMAD) and Standard Deviation (STD) metrics. As a result of the comprehensive analysis, it was found that the models performed similarly in general, but the LightGBM model was slightly behind the others. The considerable results around 0.81-0.83 as R² and ~ 0.09 as RMS and STD clearly showed that RGB cameras can be a lower-cost alternative solution for generating NDVI maps in agricultural studies when supported by machine learning models.  \n1. Introduction  \nToday, unmanned aerial vehicles (UAVs) have diverse applications, including disaster monitoring, land cover and land use (LULC) mapping, crop health assessment, urban heat map production, and various hobby uses (Ahmed et al., 2008; Do et al., 2018; Cho et al., 2023; Saponaro and Tarantino, 2022) . Their potential to record high-resolution data from lower altitudes has paved the way for their use, especially for monitoring agricultural fields. Agricultural practices are gaining importance day by day in order to increase productivity. This application area, which forms the basis for many research topics, makes frequent use of images recorded by advanced cameras and indices derived from them. The normalized difference vegetation index (NDVI), which provides information on vegetation and plant health, is among the main resources commonly used in precision agriculture practices (Houborg and McCabe, 2016; Mahajan and Bundel, 2016) . This index, calculated as the ratio of the nearinfrared and red bands, cannot be obtained with standard camera equipment that detects in the visible spectrum range (0.4 µm-0.7 µm) . This situation brings forth the need for multispectral (MS) cameras, which increases the cost.  \nMS UAVs have proven effectiveness in detecting diseases and monitoring crop development, thank","cbCairYPpoeKgIR3","https://ap.wps.com/l/cbCairYPpoeKgIR3","pdf",1708868,2,1,6,"English","en",105,"# Abstract\n# Introduction\n## NDVI and UAV imagery for precision agriculture\n## Limits of standard RGB cameras and need for multispectral sensing\n## Prior RGB-based NDVI prediction research\n## Pixel-level model-based evaluation using MS reference\n# Methods and Model Evaluation (from abstract)","[{\"question\":\"How is model performance measured in the NDVI prediction results?\",\"answer\":\"Performance is analyzed using R² along with error and dispersion metrics including Root Mean Square Error (RMSE), Normalized Median Absolute Deviation (NMAD), and Standard Deviation (STD).\"}]","NDVI Prediction with RGB UAV Imagery Utilizing Advanced Machine Learning Regression Models | PDF",1785944861,15,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":79,"head_meta":81,"extra_data":83,"updated_unix":29},"ndvi-prediction-with-rgb-uav-imagery-utilizing-advanced-machine-learning-regression-models","",{"@graph":37,"@context":78},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/ndvi-prediction-with-rgb-uav-imagery-utilizing-advanced-machine-learning-regression-models/128105/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72],{"name":73,"@type":74,"acceptedAnswer":75},"How is model performance measured in the NDVI prediction results?","Question",{"text":76,"@type":77},"Performance is analyzed using R² along with error and dispersion metrics including Root Mean Square Error (RMSE), Normalized Median Absolute Deviation (NMAD), and Standard Deviation (STD).","Answer","https://schema.org",{"og:url":52,"og:type":80,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":82,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":85},[86,90,94,98,103,107,112,115,120,123,127],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":87,"show_sort_weight":88,"slug":89},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":91,"show_sort_weight":92,"slug":93},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Exam",70,"exam",{"id":99,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},5,"Comic",60,"comic",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":99,"slug":130},19,"General","general"]