Class imbalance aware deep semantic segmentation framework for weed and tobacco crops in UAV imagery

For accurate pesticide application in precision farming, weeds and tobacco plants must be detected to efficiently apply pesticides to weedy areas. There is potential for automated, precise weed and tobacco detection using unmanned aerial vehicle (UAV)-based imaging. Semantic segmentation is a challenge that can be applied to accurately detect weeds in crop field images. Deep learning-based semantic segmentation techniques promise higher accuracy than prior approaches for pixel-level categorizati