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Öğe Accurate estimation of sorghum crop water content under different water stress levels using machine learning and hyperspectral data(Springer, 2023) Tunca, Emre; Koksal, Eyup Selim; Ozturk, Elif; Akay, Hasan; Taner, Sakine CetinThis study investigates the effects of different water stress levels on spectral information, leaf area index (LAI), and the performance of three machine learning (ML) algorithms in estimating crop water content (CWC) of sorghum. The results show that the spectral reflectance of sorghum varies with growth stage and irrigation treatment, but consistent patterns are observed for each treatment. The LAI of sorghum gradually increased throughout the growth stages, with the most significant variation observed during the flowering stage. In this study, three machine learning-based regression models, namely, extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM), were utilized to estimate sorghum CWC using hyperspectral measurements. Recursive feature elimination (RFE) method was used to select the optimal spectral reflectance wavelengths for the ML models, and principal component analysis (PCA) was used to reduce the dimensionality of the hyperspectral data. The results indicated that the RF model achieved the highest R-2 (0.90) and lowest of RMSE (56.05) value using selected wavelengths, while the XGBoost model demonstrated superior accuracy and reliability in estimating CWC using dimensionality-reduced hyperspectral data (r = 0.96, RMSE = 45.77). Also, the study highlights the importance of vegetation index (VI) in CWC estimate. Some VIs, such as NDVI and MSAVI, performed poorly, while others, such as CL_Rededge and EVI, performed better. The study provides valuable insights into the effects of water stress levels on spectral information, LAI, and the performance of ML algorithms in estimating the CWC of sorghum. The findings have significant implications for precision agriculture, as accurate and reliable estimates of CWC can help farmers optimize irrigation and fertilizer applications, leading to improved crop yields and resource efficiency.Öğe Calibrating UAV thermal sensors using machine learning methods for improved accuracy in agricultural applications(Elsevier, 2023) Tunca, Emre; Koksal, Eyup Selim; Taner, Sakine CetinAccurate temperature measurements are essential for detecting crop stress, managing irrigation, and monitoring vegetation health. However, various factors can affect thermal sensors that can introduce measurement errors. To address this, machine learning (ML) algorithms were used to calibrate unmanned air vehicle (UAV) thermal sensor measurements. In this study, commercially available two different types of UAV thermal sensors, including Micasense Altum and Flir Duo Pro-R (FDP-R), have been tested and evaluated its performance by comparing the calibrated ground thermal measurements. For this purpose, five different ML algorithms, namely Random Forest, Support Vector Machine, K-NN and XGBoost, were used to calibrate UAV thermal sensors. Results showed that, after thermal calibration with XGBoost, the RMSE decreased by 2.84 degrees C (from 4.23 degrees C to 1.39 degrees C) for Micasense Altum and by 2.51 degrees C (from 3.84 degrees C to 1.33 degrees C) for FDP-R, while R2 increased from 0.89 to 0.96 for Micasense Altum and from 0.87 to 0.94 for FDP-R. In addition, we conducted correlation analyses between the calibrated temperature measurements and various sorghum phenotype parameters, such as leaf area index, crop height, and soil moisture. The results indicate that both sensors have performed well in terms of correlation coefficients. Micasense Altum has shown slightly better performance for crop height and soil moisture (r = -0.78 and r = -0.59, respectively), while FDP-R has performed better for leaf area index (r = -0.70). This study demonstrates the potential of using calibrated UAV thermal sensors for precision agriculture tasks and highlights the importance of validating the calibration with ground measurements.Öğe Evaluating the impact of different UAV thermal sensors on evapotranspiration estimation(Elsevier, 2024) Tunca, Emre; Koksal, Eyup SelimAccurate evapotranspiration (ET) estimation is vital for precise irrigation management. Remote sensing provides a unique method for obtaining spatial and temporal ET information. With technological advancements, several unmanned aerial vehicle (UAV) thermal sensors have been developed. However, the impact of thermal sensors on ET estimation is unclear. This study evaluated the impact of different UAV thermal sensors, including Micasense Altum and Flir Duo Pro-R (FDP-R), on ET estimation using the Two Source Energy Balance (TSEB) model. A field experiment was conducted during the 2021 sorghum growing period, with irrigation treatments consisting of four different regimes: full irrigation (S1), 70 % of S1 (S2), 40 % of S1 (S3), and rainfed (S4). The results revealed no statistically significant differences between the estimated ET values using Micasense Altum and FDP-R thermal sensors. The TSEB model's performance was entirely satisfactory for full irrigation, with RMSE values of 5.63 mm for Micasense Altum and 7.17 mm for FDP-R, in 10 days. However, the accuracy deteriorated with increasing water stress, reaching 29.02 mm for Micasense Altum and 25.12 mm for FDP-R, in 10 days in rainfed plots. The study results highlight the capability of both Micasense Altum and FDP-R thermal sensors to provide comparable ET estimates, particularly under full irrigation conditions. However, the decline in accuracy with increased water stress underlines a potential limitation of the TSEB model when applied to varying irrigation regimes. These insights emphasize the importance of adjustment of TSEB input parameters such as alpha PT coefficient, resistance terms etc. and sensor technologies, particularly in water-stressed environments, to ensure accurate ET estimation. This study demonstrated the potential of high-resolution UAV thermal sensors for precision irrigation management tasks. Further studies with different thermal sensors are needed to understand this technology's benefits fully. The impact of different climate conditions on ET estimation should also be explored for accurate results.