Data Augmentation of Pseudo-Dense Images to Detect Morning Glory Regions in Soybean Fields

Morning glories prevent soybean growth and reduce yields. However, early morning glories are small and difficult to detect visually in the vast fields. Semantic segmentation effectively estimates the location of weeds using images captured by drones, but a large amount of image data is required to ensure high prediction accuracy, and an open dataset of plants with a wide variety of species is limited. In this research, we propose an image generation system of pseudo-dense morning glory using PGGAN to increase the volume of the dataset for training. The proposed pseudo images can configure the density of multiple morning glory using the distance. As a result of including these pseudo-densely morning glories, the F2 score of the estimation was 0.404.