Yazar "Koksal, E. S." seçeneğine göre listele

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    Crop height estimation of sorghum from high resolution multispectral images using the structure from motion (SfM) algorithm
    (Springer, 2023) Tunca, E.; Koksal, E. S.; Taner, S. Cetin; Akay, H.
    Crop height (CH) is the key indicators of crop growth, biomass and yield. However, obtaining CH information with manual measurement is inefficient for larger areas. High-resolution unmanned air vehicle (UAV) images offer a new alternative to traditional CH measurements. In this study, we compared three approaches to estimate sorghum CH using high-resolution multispectral images based on structure from motion (SfM) algorithm and spectral vegetation indices. In the first approach, CH was estimated based on the difference between the Digital Surface Model (DSM) map and Digital Terrain Model (DTM) map generated from UAV images captured immediately after the sowing. In the second approach, DTM was generated from DSM. In the last approach, CH was estimated using the spectral vegetation indices. High-resolution multispectral images were obtained at 40 m above ground level elevation. Ground control points were laid around the study area, and these point positions were determined using a GPS device. DSM and DTM images were generated from 3D point cloud data and the SfM algorithm. Results showed that the SfM technique could estimate sorghum CH accurately using DSM, DTM and GCPs (R2 = 0.97, RMSE = 8.77 cm, MAPE = 5.98%). Also, a high correlation was observed between estimated and measured sorghum CH using DTM maps generated from DSM maps (R2, RMSE, MAPE were 0.94, 12.2 cm, 6.66%). Moreover, GNDVI was the best vegetation index to estimate sorghum CH (R2 = 0.81, RMSE = 24.6 cm, MAPE = 12.56%). Overall, this study demonstrates the UAV potential for CH estimates and reducing the cost of obtaining CH information.
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    Novel machine learning framework for high-resolution sorghum biomass estimation using multi-temporal UAV imagery
    (Springer, 2025) Tunca, E.; Koksal, E. S.; Akay, H.; Ozturk, E.; Taner, S. C.
    Accurate 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.