Novel machine learning framework for high-resolution sorghum biomass estimation using multi-temporal UAV imagery

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dc.authoridTunca, Emre/0000-0001-6869-9602;
dc.contributor.authorTunca, E.
dc.contributor.authorKoksal, E. S.
dc.contributor.authorAkay, H.
dc.contributor.authorOzturk, E.
dc.contributor.authorTaner, S. C.
dc.date.accessioned2025-10-11T20:48:38Z
dc.date.available2025-10-11T20:48:38Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractAccurate assessment of crop health and yield potential facilitates precise estimation of above-ground biomass (AGB). Traditional AGB estimation methods are often limited by their destructive, labor-intensive nature. This study developed a rapid, non-destructive approach to estimate sorghum AGB using high-resolution unmanned aerial vehicle (UAV) data and machine learning (ML). A two-year field experiment tested four irrigation strategies: full irrigation at 100% of crop evapotranspiration (S1), partial deficits at 75% and 50% of S1, and a rain-fed (S4). This gradient assessed the ML model's robustness across diverse conditions, yielding 216 AGB measurements reflecting variable plant responses to water stress. Multispectral and canopy height data were derived from UAV imagery collected during the sorghum growing season. Three ML algorithms-Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN)-were applied. RF outperformed others with an R2 of 0.80, RMSE of 0.78 kg m(-)2, and MAE of 0.58 kg m(-)2, followed by SVM (R2 = 0.64, RMSE = 1.08 kg m(-)2, MAE = 0.77 kg m(-)2), while K-NN showed the lowest accuracy (R2 = 0.50, RMSE = 1.26 kg m(-)2, MAE = 0.96 kg m(-)2). Optimal RF hyperparameters were identified, and estimated AGB aligned closely with ground measurements, showing no significant differences. Spatial AGB maps effectively highlighted variability across treatments. This study demonstrates that UAV-based remote sensing combined with ML offers a reliable, non-destructive method for sorghum AGB estimation, enhancing precision agriculture applications such as irrigation management, crop monitoring, and yield prediction.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK)en_US
dc.description.sponsorshipTUBITAKen_US
dc.description.sponsorshipThe authors thank to TUBITAK for their supports.en_US
dc.identifier.doi10.1007/s13762-025-06498-y
dc.identifier.endpage13688en_US
dc.identifier.issn1735-1472
dc.identifier.issn1735-2630
dc.identifier.issue14en_US
dc.identifier.scopus2-s2.0-105004708970en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage13673en_US
dc.identifier.urihttps://doi.org/10.1007/s13762-025-06498-y
dc.identifier.urihttps://hdl.handle.net/20.500.12684/22031
dc.identifier.volume22en_US
dc.identifier.wosWOS:001484590100001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofInternational Journal of Environmental Scienceand Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectAbove ground biomassen_US
dc.subjectSorghumen_US
dc.subjectUAV time series imageryen_US
dc.subjectMachine learningen_US
dc.subjectRemote sensingen_US
dc.titleNovel machine learning framework for high-resolution sorghum biomass estimation using multi-temporal UAV imageryen_US
dc.typeArticleen_US

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