Bu çalışmada, Tekirdağ ilinde yetiştirilen elma yetiştiriciliğinde enerji kullanım etkinliğinin belirlenmesi amaçlanmıştır. Enerji kullanım etkinliği çalışması, Tekirdağ ili Merkez ilçesi Nusratlı köyündeki bir işletmede 2015 üretim sezonunda 12 da alana sahip elma bahçesinde yapılan gözlem ve ölçüm yoluyla gerçekleştirilmiştir. Girdiler içerisinde mekanizasyon enerjisinin rolü ortaya konulmaya çalışılmıştır. Hesaplanan verilere göre, elma yetiştiriciliğinde toplam enerji girdisi, toplam ürün verimi, toplam enerji çıktısı, enerji çıktı/girdi oranı, özgül enerji, enerji verimliliği ve net enerji verimi sırasıyla 58839.65 MJ ha-1, 38370 kg ha-1, 92088.00 MJ ha-1, 1.56, 1.53 MJ kg-1, 0.64 kg MJ-1 ve 33248.35 MJ ha-1 olarak belirlenmiştir. Sonuç olarak, elma yetiştiriciliğinde genel enerji girdileri içerisinde en fazla enerji tüketim sırasıyla gübre enerjisi, yakıt-yağ enerjisi, kimyasallar, makine, insan işgücü ve sulama enerjisi olarak belirlenmiştir.

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.

Plant diseases reduce yield and quality in agricultural production by 20–40%. Leaf diseases cause 42% of agricultural production losses. Image processing techniques based on artificial neural networks are used for the non-destructive detection of leaf diseases on the plant. Since leaf diseases have a complex structure, it is necessary to increase the accuracy and generalizability of the developed machine learning models. In this study, an artificial neural network model for bean leaf disease detection was developed by fusing descriptive vectors obtained from bean leaves with HOG (Histogram Oriented Gradient) feature extraction and transfer learning feature extraction methods. The model using feature fusion has higher accuracy than only HOG feature extraction and only transfer learning feature extraction models. Also, the feature fusion model converged to the solution faster. Feature fusion model had 98.33, 98.40 and 99.24% accuracy in training, validation, and test datasets, respectively. The study shows that the proposed method can effectively capture interclass distinguishing features faster and more accurately.

Bu çalışmada 5mm dilimindeki patatesin mikrodalga bantlı kurutucuda mikrodalga gücünün (1500W ve 2100W) ve bant hızının (0,175, 0,210 ve 0,245 m/dak) kurutma zamanı, renk değişimi ve enerji tüketimine etkisi araştırıldı. Elde edilen sonuçlara göre mikrodalga gücünün artması ve bant hızının azalması ile enerji tüketimi azalmıştır. Dokuz adet kurutma modelinin uygunluğunu belirlemek için deneysel ve teorik nem oranları dikkate alınarak korelasyon katsayısı (r), standart hata (e_s) ve (χ²) hesaplandı. Tüm kurutma şartları için Page modelin en uygun model olduğu belirlenmiştir. Efektif difüzyon katsayısı 2,958.10^-7 ve 3,587.10^-6 m²/s aralığında hesaplanmıştır. Kurutma süresince en az enerji 0,175m/dak ve 2100W gücünde belirlenmiştir.

In this study, the performance of the Genetic Algorithm (GA) in optimizing the agricultural drone flight route was compared with the Greedy Algorithm, revealing that GA produce routes that are, on average, 17.44 % more efficient. This efficiency, measured over 500 generations in a static field model, suggests substantial potential for saving resources and time in agricultural operations. Despite the effectiveness of the GA, its computational intensity limits real-time field applications, but offers advantages in offline route planning for pre-mapped areas. A t-test between flight lengths created by the algorithms highlighted a significant difference, with a p-value of approximately 7.18×10−9, indicating the GA's superior performance. Future research should aim to bridge the gap between the simplified binary field model used in simulations and the complexities of real-world agricultural landscapes to improve the practical deployment of GAs in drone route optimization.

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 R2 (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).