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    Effect of Unregistreted Fungicides to Fusarium culmorum on Wheat
    (Univ Namik Kemal, 2018) Koycu, Nagehan Desen; Sukut, Fusun
    The importance of pathogen in World and Turkey has been revealed when it was released that F. culmorum causes head blight and seedling in wheat (FHB) carrying by seed or soil. It threatens the health of human and animals due to their toxins released by pathogens in grains. The objective of this study is to identify the effectiveness of tebuconazole+metalaxyl-M (Maxim XL 035) and fludioxonil+metalaxyl-M (Certigor 050 FS) unregistreted to the pathogens on wheat. There are a few number of fungicides registreted for seed pathogens of the wheats. The sensitivity of pathogen against the fungicide has been identified by analyzing the effective concentration (EC50) on mycelial development of F.culmorum (S-14) isolate known as pathogen. The experiments of efficacy of fungicides on pathogen were carried out both in petri dishes and pots under controlled conditions. In the experiments, the seeds which have been infected by S-14 isolate of the pathogens and obtained from the uninfected Flamura-85 wheat type were used. In vivo experiments were conducted by amended and unamended seeds with fungicides and planting to the sterilized soil or planting the clean disinfected seeds with fungicides by infecting the pathogens to sterilized soil at 1x10(6) spores/ml. At the end of the experiment, germination rate, plant length, fresh and dry weight measurements, disease severity rate of seeds were measured.EC50 values for tebuconazole+metalaxyl-M and fludioxonil+metalaxyl-M fungicides of pathogen have been identified respectively as 0,55 and 1,57 mu g/ml. It has been found that there was a significant difference between the germination, root, coleoptile lengths and disease severity when both fungicides were compared with control in the in vitro tests. It was determined that there was a significant difference on plant length, wet and dry weight, and disease severity of both fungicides in vivo tests compared to the control.
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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.