Yazar "Ozturk, Elif" seçeneğine göre listele
Listeleniyor 1 - 3 / 3
Sayfa Başına Sonuç
Sıralama seçenekleri
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 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 letinAccurate 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.Öğe Threat in water for drinking and domestic use: Nontuberculous mycobacteria(Wolters Kluwer Medknow Publications, 2021) Atik, Dursun; Oksuz, Sukru; Ozturk, Elif; Caliskan, Emel; Akar, Nida; Sungur, Mehmet AliObjective: Nontuberculous mycobacteria (NTM) have been recognized as a diverse group of organisms that are ubiquitous in environmental sources. In most regions of the world, NTM are not reportable as a public health disease, so epidemiological data are not easily available. However, data in published studies note increasing trends at the rate of NTM isolation from different geographic regions of the world. Increasing NTM isolation may have important public health implications. The aim of our study is the investigation of NTM from water resources and networks in Duzce, Turkey. Methods: NTM are common in water resources and water networks. They can cause waterborne infections in humans. A total of 120 water samples measured of chlorine and pH levels were decontaminated and filtered. Then, the filters were placed in the culturing media. Statistical Analysis Used: Chi-square and t-test were used for the statistical analysis. Results: NTM were detected in 20 (16.6%) samples. Nine of them (45%) were Mycobacterium fortuitum, three (15%) were Mycobacterium gordonae, three (15%) were Mycobacterium szulgai, two (10%) were Mycobacterium lentiflavum, two (10%) were Mycobacterium chelonae, and one (5%) was Mycobacterium peregrinum. Conclusions: These environmental bacteria can cause serious illnesses in both immunocompetent and especially immunocompromised individuals. For the correct treatment of these patients, it is important to determine NTM in clinical samples. Surveillance is necessary to know the source of NTM infection, to identify and type the strains, and to establish effective control measures such as disinfection, maintenance, and modernization of water systems.
