Crop detection; LiDAR; Machine Learning; UAS; High resolution; Local maximum filtering; Machine-learning; Maize detection; Plant detections; Plant level; Remote sensing technology; Unmanned aircraft system; Computer Science Applications; Earth and Planetary Sciences (all)
Abstract :
[en] As unmanned aircraft systems (UAS) remote sensing technology has advanced, providing unprecedented resolution, crop status at the individual plant level has become popular. Often plant detection is performed using high resolution RGB cameras that utilize algorithms and machine learning methods centered around trained pixel patterns of object textures. Similar methods with UAS LiDAR are not as explored considering their more recent UAS adaptation and the significantly larger price tag. Methods that have been created center around individual tree detection using crown delineations utilizing the height information and local maximum filtering. This study explores if this methodology can be used in a similar way for crops such as maize.
Bates, Jordan Steven; Institute of Bio-and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany
Jonard, François ; Université de Liège - ULiège > Département de géographie ; Institute of Bio-and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany
Vereecken, Harry; Institute of Bio-and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany
Montzka, Carsten; Institute of Bio-and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany
Language :
English
Title :
UAS LiDAR Local Maximum Filtering for Individual Maize Detection
Publication date :
2022
Event name :
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Event place :
Kuala Lumpur, Mys
Event date :
17-07-2022 => 22-07-2022
Audience :
International
Main work title :
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Publisher :
Institute of Electrical and Electronics Engineers Inc.
The Institute of Electrical and Electronics Engineers Geoscience and Remote Sensing Society (GRSS)
Funding text :
Research partially funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy-EXC 2070 –390732324.
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