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    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 Cetin
    This 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.
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    Accurate leaf area index estimation in sorghum using high-resolution UAV data and machine learning models
    (Pergamon-Elsevier Science Ltd, 2024) Tunca, Emre; Koksal, Eyuep Selim; Ozturk, Elif; Akay, Hasan; Taner, Sakine letin
    Accurate 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.
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    Calibrating UAV thermal sensors using machine learning methods for improved accuracy in agricultural applications
    (Elsevier, 2023) Tunca, Emre; Koksal, Eyup Selim; Taner, Sakine Cetin
    Accurate 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.
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    Evaluating the impact of different UAV thermal sensors on evapotranspiration estimation
    (Elsevier, 2024) Tunca, Emre; Koksal, Eyup Selim
    Accurate 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.
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    Evaluating the performance of the TSEB model for sorghum evapotranspiration estimation using time series UAV imagery
    (Springer, 2023) Tunca, Emre
    Evapotranspiration (ET) is a vital process involving the transfer of water from the Earth's surface to the atmosphere through soil evaporation and plant transpiration. Accurate estimation of ET is important for a variety of applications, including irrigation management and water resource planning. The two-source energy balance (TSEB) model is a commonly used method for estimating ET using remotely sensed data. This study used the TSEB model and high-resolution unmanned aerial vehicle (UAV) imagery to estimate sorghum ET under four different irrigation regimes over two growing seasons in 2020 and 2021. The study also validated net radiation (Rn) flux through hand-held radiometer measurements and compared the estimated ET with a soil water balance model. The study outcomes revealed that that the TSEB model capably estimated Rn values, aligning well with ground-based Rn measurements for all irrigation treatments (RMSE = 32.9-39.8 W m(-2) and MAE = 28.1-35.2 W m(-2)). However, the TSEB model demonstrated robust performance in estimating ET for fully irrigated conditions (S1), while its performance diminished with increasing water stress (S2, S3, and S4). The R-2, RMSE, and MAE values range from 0.64 to 0.06, 10.94 to 17.04 mm, and 7.09 to 11.43 mm, respectively, across the four irrigation treatments over a 10-day span. These findings not only suggest the potential of UAVs for ET mapping at high-resolution over large areas under various water stress conditions, but also highlight the need for further research on ET estimation under water stress conditions.