This study has been carried out in 2017-2018 in order to determine seed dormancy and effective germination depth wild mustard (Sinapis arvensis L.). The in-vitro dormancy breaking experiments (tip breaking, sanding, H2SO4 application, holding in flowing and still water, GA3, KNO3 and GA3+KNO3 combination application) has been applied to wild mustard seeds collected from wheat field in Tokat province and has been applied to wild mustard seeds collected from wheat field in Tokat province and the most effective method was determined as 1000 ppm GA3+KNO3 with 98% impact on seed germination at 15°C within 72 hours. In contrast germination rate has been calculated as 5% in control plants. Furthermore 15°C was assessed as optimum temperature for seed germination was the most effective temperature and during depth studies 100% of wild mustard seeds germinated at 3-5 cm. Because of the difficulies with the work with seeds and plants that have dormancy, these data will contribute future studies.

The detection of weeds with computer vision without the help of an expert is important for scientific studies and other purposes. The images used for the detection of weeds are recorded under controlled conditions and used in image processing-deep learning methods. In this study, the images of 3-4-leaf (true-leaf) periods of the wild mustard (Sinapis arvensis) plant, which is the critical process for chemical control, were recorded from its natural environment by a drone. The datasets were included 50-100-250-500 and 1 000 raw images and were augmented by image preprocessing methods. Totally 12 different augmentation methods used and datasets were examined for understand how to affects the numbers of images on training-validation performance. YOLOv5 was used as a deep learning method and results of the datasets were evaluated with the Confusion Matrix, Metrics-Precision, and Train-Object Loss. For results of Confusion Matrix where 1 000 images gave the highest results with TP (True Positive) 80% and FP (False Positive) 20%. The TP-FP ratios of 500, 250, 100 and 50 image numbers were respectively; 65%-35%, 43%-57%, 0%-100% and 0%-100%. With 100 and 50 images, the system did not show any TP success. The highest metrics-precision ratio was found 92.52% for 1 000 images set and for 500 and 250 image sets respectively; 88.34% and 79.87%. The 100 and 50 images datasets did not show any metrics-precision ratio. The minimum object loss ratio was 5% at 50th epochs in the 100 images dataset. This dataset was followed by other 50, 250, 500, and 1 000 images respectively; 5.4%, 6.14%, 6.16%, and 8.07%.