Using Fully Convolutional Networks for Rumex Obtusifolius Segmentation, a Preliminary Report

Image segmentation of specific plants is an important task in precision farming. Several influences such as changing light, varying arrangement of leaves and similarly looking plants are challenging. We present a solution for segmenting individual Rumex obtusifolius plants out of complicated natural scenes in grassland from 2D images. We are making use of a fully convolutional deep neural network (FCN) trained with hand labeled images. The proposed segmentation scheme is validated with images taken under outdoor conditions. The overall masks segmentation rate is 84.8% measured by the dice coefficient. Approximately half of the experiments show segmentation rates of individual plants higher than 88%. The developed solution is therefore a robust method to segment Rumex obtusifolius plants under real-world conditions in short time.