Vision System for Robotized Weed Recognition in Crops and Grasslands

In this paper, we introduce a novel vision system for robotized weed control on various weed recognition tasks. Initially, we present a robotic platform and its camera setup, that can be used in crop-based and grassland-based weed control tasks. Then, we develop our proposed vision system for robotic application, using a weed recognition framework. The resulting system derives from a sequence of state-of-the-art processes including image preprocessing, feature extraction and detection, codebook learning, feature encoding, image representation and classification. Our novel system is optimized using a dataset which represents a crop-based weed control problem of thistles in sugar beet plantation. Moreover, we apply the proposed vision system to a grassland-based weed recognition problem, the control of the Broad-leaved Dock (Rumex obtusifolius L.). It is experimentally shown that our proposed visual system yields state-of-the-art recognition in both examined datasets, while presenting advantages in terms of autonomy and precision over competing methodologies.