Koç Mehmet Tuğrul

Selecting ideal drought-tolerant wheat varieties requires a holistic synthesis of digital phenotypes, molecular markers, and agronomic indices. This study evaluated 16 wheat genotypes for drought tolerance by integrating digital phenotyping (UAV-based thermal imaging), molecular data (DREB gene expression profiles), and 12 agronomic indices. While vegetative DREB accumulation remained mostly homogeneous, the generative stage triggered pronounced transcriptional shifts, and late-stage thermal screening revealed highly significant genotypic differences during grain filling. A multivariate PCA biplot identified early canopy temperature differences during tillering (ΔCT_TL) as the most informative non-destructive selection indicator. ΔCT_TL showed a strong positive association with terminal yield stability metrics (YSI and RSI) and a marked negative relationship with the drought sensitivity index (SDI). This early canopy temperature regulation contributed to the maintenance of yield stability in the modern hexaploid variety MFTBY-T and advanced tetraploid lines OR2-T and OR4-S. In contrast, poorly adapted ancient varieties (P5-S and S3-S) exhibited high drought sensitivity accompanied by pronounced late-stage induction of DREB1 and DREB2, suggesting a delayed stress-response mechanism activated under severe tissue dehydration. Conversely, the modern tetraploid variety KZLTN-T and hexaploid landraces appeared to rely on an early vegetative molecular priming strategy. These findings suggest that breeding programs should prioritize the incorporation of vegetative transcriptional traits associated with effective canopy temperature homeostasis into elite genetic backgrounds.

Background: Agricultural production is crucial for nutrition, but it frequently faces challenges such as decreased yield, quality, and overall output due to the adverse effects of diseases and pests. Remote sensing technologies have emerged as valuable tools for diagnosing and monitoring these issues. They offer significant advantages over traditional methods, which are often time-consuming and limited in sampling. High-resolution images from drones and satellites provide fast and accurate solutions for detecting and diagnosing crops’ health and identifying pests and diseases affecting them. Methods: The research focused on the early detection of Cercospora leaf spot (Cercospora beticola Sacc.) and powdery mildew (Erysiphe betae (Vaňha) Weltzien), which cause significant economic losses in sugar beet before visible symptoms emerge. The study was accomplished by capturing images of uncrewed aerial vehicle (UAV) in field conditions. To effectively evaluate different detection methods in agricultural contexts, the study targeted two key areas: (1) monitoring Cercospora in fields without pesticide application, utilizing the Metos climate station early warning system alongside UAV-based image analysis, and (2) monitoring powdery mildew, which involved visual disease detection and targeted spraying based on UAV image processing. Trial plots were established for this purpose, with six replications for each method. Results: UAV-based images show that Normalized Difference Vegetation Index values in leaves decreased before disease onset. This change is an important warning sign for the emergence of the disease. Additionally, the study demonstrated that early detection of diseases is possible using K-nearest neighbors and logistic regression algorithms, exhibiting high discrimination and predictive accuracy.

Early diagnosis of diseases in agriculture is an important factor in reducing the negative environmental impacts by effectively and economically managing the losses caused by these diseases and reducing the use of chemicals. There are different options within the scope of remote sensing for the early detection of diseases. Among these, choosing a method that can detect diseases accurately without harming the plant and the environment is important. Today, positive developments have been made toward non-invasive and effective detection of diseases with thermal camera-based image processing techniques. In this context, there is potential for disease detection with data collection, image processing, and the determination of the characteristics of disease agents through thermal imaging. The research was based on Cercospora leaf spot (Cercospora beticola Sacc.) diseases which have significant economic loss potential in sugar beet. The effectiveness of the proposed method was evaluated in experiments involving Cercospora beticola, utilizing a climate station early warning system and UAV-based thermal images across three subjects and six replicate field trial plots. Analyses were made for the early detection of diseases by comparing thermal images taken from the field with multispectral images taken simultaneously. It was investigated whether it was possible to diagnose the disease early before physical symptoms were seen using image processing and machine learning methods. The variability of leaves was analyzed using field images, thermal images, and machine learning algorithms. Thermal imaging enables the rapid detection of potential disease development by measuring increases in leaf temperature in infrared wavelengths. However, a significant limitation of this method in practice is its sensitivity to climate factors such as air temperature and humidity, which can cause rapid fluctuations in the index. This study compared five machine learning algorithms based on four key metrics. MS imaging achieved about 25% higher accuracy in predicting early disease than TE imaging. This study indicates that thermal imaging provides valuable information but is not as effective as multispectral imaging in detecting early-stage stress factors related to diseases.

Traditionally, diagnosis and monitoring of agricultural diseases are carried out through on-site observation and inspection. These methods are time-consuming and may represent limited samples. Therefore, remote sensing technology has become an important tool in disease detection and monitoring in agriculture. In the research, Cercospora leaf spot (Cercospora beticola sacc.) disease, which cause significant economic losses in sugar beet production, were detected in the early stages using machine learning algorithms using non-invasive multispectral images taken with UAV under field conditions is intended to be determined. The research was fulfilled using images from the grower fields in two regions where the disease was observed intensively. Index value data from digital surface model maps created by processing the images taken were used as training and test data. Numerical data was tested using five different supervised machine learning methods. The success of the analyzed models in predicting disease formation from the index values obtained from the images taken and the physiological changes that occur before the disease agents appear on sugar beet leaves was over 70%. Among the models compared in the study, the k-nearest neighbor classifier (KNN) model gave the highest success in both diseases, with 83% accuracy and 76% and 86% f1-score values. The support vector machines model followed the KNN model with 77% accuracy, 75%, and 86% f1-score values. According to the results of the research, it has been revealed that plant diseases have the potential for pre-symptomatic detection, and by processing the images obtained with UAV-based MS images, it is possible to detect diseases in the early period.

Unmanned aerial vehicles, previously used for military purposes, have started to be used for civilian purposes since the 2000s. With the widespread use of unmanned aerial vehicles, mostly used for defense purposes, they have turned into flying vehicles called drones. Today, it is used for different purposes such as taking pictures, taking images from inaccessible places, competitions, and having a good time. However, it continues to evolve, playing an important role in social media and inter-company competition. In recent years, studies have continued on small drones that can fly autonomously, especially in closed areas, and assist people. The shrinkage and cost reduction of electronic components such as microprocessors, sensors, batteries, and wireless communication units shows that drones smaller than 1 kg will soon occur in many areas of our daily life at much more affordable prices. This rapid change, development, and proliferation of drones also raise privacy and security concerns. While obtaining images of people and institutions in supposedly private environments with such technological devices emphasizes individual security, its use in urban areas and airports draws attention to social security. The rapid developments in this area necessitate the necessary regulations and controls to prevent accidents and other problems due to the growth in use and loss of control.