Ursolic acid isolated from the leaves and stems of Duboisia myoporoides (Solanaceae) was bioassayed by leaf disc method for feeding deterrence using Spilosoma obliqua and Spodoptera litura as test insects. This compound was proved to be a potent antifeedant under laboratory conditions. Azadirachtin was used as standard. Ursolic acid produced 90.12% and 91.96% inhibition at 5000 ppm concentration, respectively, against S. obliqua and S. litura.
Eight different varieties like QPM, Rampur Composite, RML, Mankamana-4, Arun-2, Across, Deuti and Manakamana-3 were used for varietal screening against maize weevil damage. The research was done in free-choice and no-choice conditions. Deuti variety of maize was the most susceptible variety and grain damage was recorded up to 40% whereas in long term storage condition, Across (44.81%) was the most suitable variety for weevils. The RML variety of maize was the least damaged variety and loss recorded about 18.12% in 60 days of observations. But while calculating the weight loss of the weevil, the loss 7.66% recorded in 60 days of observation in Across Variety, 6.26% in 40 days of observation in QPM and 5.06% were recorded in Deuti, whereas the lowest percent weight loss was recorded in Manakamana-4 that was 1.80% and 1.00 % in 40 days and 60 days respectively. Maximum number of F1 progenies were observed in across (74.00) and lowest were emerged in Rampur composite (32.33) and RML (32.67). The lowest germination loss was recorded in QPM (8.00%), followed by Rampur Composite (10.00%) and RML (12.67%) respectively.
In the current scenario on the increasing number of motor vehicles day by day, so traffic regulation faces many challenges on intelligent road surveillance and governance, this is one of the important research areas in the artificial intelligence or deep learning. Among various technologies, computer vision and machine learning algorithms have the most efficient, as a huge vehicles video or image data on road is available for study. In this paper, we proposed computer vision-based an efficient approach to vehicle detection, recognition and Tracking. We merge with one-stage (YOLOv4) and two-stage (R-FCN) detectors methods to improve vehicle detection accuracy and speed results. Two-stage object detection methods provide high localization and object recognition precision, even as one-stage detectors achieve high inference and test speed. Deep-SORT tracker method applied for detects bounding boxes to estimate trajectories. We analyze the performance of the Mask RCNN benchmark, YOLOv3 and Proposed YOLOv4 + R-FCN on the UA-DETRAC dataset and study with certain parameters like Mean Average Precisions (mAP), Precision recall.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.