Accurate leaf area index estimation in sorghum using high-resolution UAV data and machine learning models

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dc.authoridTunca, Emre/0000-0001-6869-9602en_US
dc.authorscopusid57204446671en_US
dc.authorscopusid24344113900en_US
dc.authorscopusid57214484479en_US
dc.authorscopusid56868366700en_US
dc.authorscopusid58042661200en_US
dc.authorwosidTunca, Emre/IQT-3077-2023en_US
dc.contributor.authorTunca, Emre
dc.contributor.authorKoksal, Eyuep Selim
dc.contributor.authorOzturk, Elif
dc.contributor.authorAkay, Hasan
dc.contributor.authorTaner, Sakine letin
dc.date.accessioned2024-08-23T16:04:35Z
dc.date.available2024-08-23T16:04:35Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractAccurate estimation of leaf area index (LAI) is essential for precision agriculture, yet traditional ground-based measurements are destructive, time-consuming, and limited in scale. This study aimed to address the need for rapid, non-destructive LAI monitoring over large areas by evaluating unmanned aerial vehicle (UAV) data and machine learning (ML) models. A field experiment with four irrigation treatments was conducted to obtain wide range of LAI values over two years. Multispectral and thermal UAV images were acquired throughout the growing season along with destructive LAI measurements. Five ML algorithms, including K-Nearest Neighbors (K-NN), Extra Trees Regressor (ETR), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR) were tested. Also, feature selection procedures were implemented to obtain useful information among the features used for the ML model. Results showed the K-NN model achieved the highest accuracy (R2 = 0.97, RMSE = 0.46, MAE = 0.197), followed by ETR. The analysis of feature selection revealed that the combination of Normalized Difference Vegetation Index (NDVI) and canopy height (NDVI x Hc) product had the highest importance, followed by Soil-Adjusted Vegetation Index (SAVI) and Green Normalized Difference Vegetation Index (GNDVI). Also, utilizing vegetation indices calculated from multiple spectral bands proved to be more effective than using individual bands alone. Overall, the study demonstrates that UAV data and ML techniques can estimate sorghum LAI precisely to support precision agriculture applications. Moreover, using low-cost UAVs equipped with multispectral sensors presents a cost-effective and reliable method for LAI estimation using ML models.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [118O831]en_US
dc.description.sponsorshipThis study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK, Project Number: 118O831) .en_US
dc.identifier.doi10.1016/j.pce.2023.103537
dc.identifier.issn1474-7065
dc.identifier.issn1873-5193
dc.identifier.scopus2-s2.0-85181171729en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1016/j.pce.2023.103537
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14281
dc.identifier.volume133en_US
dc.identifier.wosWOS:001150650500001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofPhysics And Chemistry of the Earthen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSorghumen_US
dc.subjectLAIen_US
dc.subjectUAVen_US
dc.subjectMultispectralen_US
dc.subjectMachine learningen_US
dc.subjectVegetation Indexesen_US
dc.subjectCanopyen_US
dc.subjectEvapotranspirationen_US
dc.subjectReden_US
dc.titleAccurate leaf area index estimation in sorghum using high-resolution UAV data and machine learning modelsen_US
dc.typeArticleen_US

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