TY - GEN
T1 - Vision system for robotized weed recognition in crops and grasslands
AU - Kounalakis, Tsampikos
AU - Triantafyllidis, Georgios A.
AU - Nalpantidis, Lazaros
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85031827815&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-68345-4_43
DO - 10.1007/978-3-319-68345-4_43
M3 - Article in proceeding
AN - SCOPUS:85031827815
SN - 978-3-319-68344-7
T3 - Lecture Notes in Computer Science
SP - 485
EP - 498
BT - Computer Vision Systems
PB - Springer
T2 - 11th International Conference on Computer Vision Systems, ICVS 2017
Y2 - 10 July 2017 through 13 July 2017
ER -