Wheat is one of the most important crops grown all around the world. Weeds cause significant yield loss and damage to wheat and their control is generally based on herbicide application. Regular use leads to herbicide resistance in weeds. This study aims to reveal molecular detection of Sinapis arvensis resistance mutation against ALS inhibiting herbicides. For this purpose, survey studies have been carried out in wheat growing areas in Amasya, Çorum, Tokat, and Yozgat provinces and wild mustard seeds have been collected from 310 different fields. According to bioassay tests with tribenuron-methyl, 13 of these populations, have not been affected by the registered dose of herbicide. When survived populations were subjected to dose-effect study and herbicides were applied at 26-fold, the highest and lowest resistance coefficients were determined as 7.2 (A-007) and 1.02 (T-034) respectively. In addition, B domain region from ALS gene was amplified and analyzed in molecular studies to determine point mutation in wild mustard against ALS herbicides. The PCR products were sequenced and target-site mutation to Leucine was observed at Trp-574 amino acide. In the study, point mutation in Trp-574 amino acide and Trp-574 Leu mutation in Sinapis arvensis have been detected for the first time in Turkey.

Abstract-The weed flora of vineyards in northwestern

In this study, antibacterial effects of semi-parasitic plant common mistletoe (Viscum album L.), collected from different woody host, extracts on the tomato bacterial cancer and wilt disease agent Clavibacter michiganensis subsp. michiganensis, pepper and tomato bacterial leaf spot disease agent Xanthomonas axonopodis pv. vesicatoria and tomato bacterial leaf spot disease agent Pseudomonas syringae pv.tomato were determined. The common mistletoe collected from pine, pear, acacia and mahaleb trees. The leaves and stems water extracts of common mistletoe were added to Nutrinet agar medium before autoclaving at the final concentrations of 1%, 2.5%, 5% and 10%. The bacterial concentration was adjusted to 108 cfu/ml with spectrophotometer to within an 0.2 at 600 nm. Then, 100 µl of bacterial inoculums were spread over the extracts amended media plates. As a control group, pathogens were plated on the unamended media. The study was established in 3 repetitions and repeated 2 times. At the end of the incubation period, bacteria growing on all treated petri dishes were collected and their density was measured in a spectrophotometer. Based on the results of the study, 1% and 2.5% concentration of the extracts obtained from leaves and stems of common mistletoe collected from different trees were not effective on the bacteria tested, while 5% and 10% concentration of them inhibited the bacterial growth completely (100%). Also, it was observed that there wasn’t difference on the pathogens on the basis of the host where mistletoe was collected. According to the results of this study conducted under in vitro conditions, in vivo studies should be carried out with the common mistletoe extract, which is effective on the bacterial pathogens.

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%.

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