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    Estimation of Monthly, Seasonal and Annual Total Solar Radiation on the Tilted Surface at Optimum Tilt Angles in Two Provinces, Turkiye
    (Univ Namik Kemal, 2023) Onler, Eray; Kayisoglu, Birol
    In solar energy systems that use solar panels, it's important to know the best tilt angle to optimize solar energy production. Monthly, seasonal, and annual optimum tilt angles were determined in this study using meteorological insolation data from many years in the provinces of Tekirdag and Konya, which are located in different regions of Turkey. At optimum tilt angles, monthly, seasonal, and annual total radiation on the tilted surface were 1516.7 kWh m(-2) year(-1), 1504.1 kWh m(-2) year(-1) and 1448.1 kWh m(-2) year(-1) in Tekirdag, respectively. In Konya, these values were 1851.4 kWh m(-2) year(-1), 1833.51 kWh m(-2) year(-1) and kWh m(-2) year(-1), respectively. In the seasonal and annual optimum tilt angles, there was an approximately 1% and 5% loss in the total radiation values on the tilted surface, respectively, according to the monthly optimum tilt angle. In addition, the coefficients of the relationship between the monthly mean daily radiation on the tilted surface and the tilt angles were determined for each month using the cubic regression model in both provinces. The Cubic regression model coefficients are computed for each month in the provinces of Tekirdag and Konya. All months in both provinces had R-2 (Coefficient of determination) values of 0.999 for the Cubic model. To determine whether there is a difference between the total amounts of radiation reaching the tilted surface for each month at the best tilt angles obtained by the two methods, the t-test was used. The monthly average daily radiation values on the tilted surface obtained by the two methods at the best tilt angles in both provinces have not been found to differ statistically (p>0.05; t=0.001).
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    Spray Characterization of an Unmanned Aerial Vehicle for Agricultural Spraying
    (Univ Philippines Los Banos, 2023) Onler, Eray; Ozyurt, Hasan Berk; Sener, Mehmet; Arat, Sezen; Eker, Bulent; Celen, Huseyin
    Sustainability and higher efficiency in crop production are possible with the use of new technologies. The use of unmanned aerial vehicles (UAVs) brings many advantages, both in terms of monitoring agricultural areas and pesticide applications, and allows for early disease and damage detection as well as its application in areas without access to conventional sprayers. This study established parameters for spraying with clean water using a DJI Agras 14 MG-1P (RTK) UAV. Droplet distribution and droplet analyses were examined in the experiments carried out at different heights (1.5, 2.0, and 2.5 m) and flow rates (10, 15, 20, 25, and 30 L/ha). Droplets were analyzed using DepositScan. Coefficients of Variation of droplet distribution decreased with the increasing spray rate. The trials with the closest values to uniformity were spraying applications made at a flight height of 2.0 m. When evaluating pesticide efficacy according to the number of droplets per unit area, insecticides and all herbicides can be effective at applications at flight heights of 1.5 and 2.0 m and spray rates of 20 L/ha. While all spraying is done at flight heights of 1.5 and 2.0 m and spray rates of 25 L/ha, fungicides are ineffective when applied from a height of 2.5 m. As a result, this study found measurements made at a 2.0 m altitude and a 20 L/ha spray rate to have the highest coverage rate and the lowest drift potential.
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    Wheat Powdery Mildew Detection with YOLOv8 Object Detection Model
    (Mdpi, 2024) Onler, Eray; Koycu, Nagehan Desen
    Wheat powdery mildew is a fungal disease that significantly impacts wheat yield and quality. Controlling this disease requires the use of resistant varieties, fungicides, crop rotation, and proper sanitation. Precision agriculture focuses on the strategic use of agricultural inputs to maximize benefits while minimizing environmental and human health effects. Object detection using computer vision enables selective spraying of pesticides, allowing for targeted application. Traditional detection methods rely on manually crafted features, while deep learning-based methods use deep neural networks to learn features autonomously from the data. You Look Only Once (YOLO) and other one-stage detectors are advantageous due to their speed and competition. This research aimed to design a model to detect powdery mildew in wheat using digital images. Multiple YOLOv8 models were trained with a custom dataset of images collected from trial areas at Tekirdag Namik Kemal University. The YOLOv8m model demonstrated the highest precision, recall, F1, and average precision values of 0.79, 0.74, 0.770, 0.76, and 0.35, respectively.