The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 329–335, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-329-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 329–335, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-329-2019

  04 Jun 2019

04 Jun 2019

AUTOMATIC GENERATION OF GEOMETRIC PARAMETERS OF INDIVIDUAL CAULIFLOWER PLANTS FOR RAPID PHENOTYPING USING DRONE IMAGES

G. J. Grenzdörffer G. J. Grenzdörffer
  • Chair for Geodesy and Geoinformatics, Universität Rostock, Germany

Keywords: UAV, Precision Farming, Crop Height, Phenotyping, CHM, Drone

Abstract. Multitemporal drone surveys are a perfect tool to determine various geometric and spectral crop parameters for rapid phenotyping in field trials. Depending on the geometric resolution and the size of the crop, information at leaf level or canopy level can be obtained. The focus of this paper is to demonstrate which geometric properties can be automatically derived from high resolution drone imagery during the vegetation period. For this research approx. 1920 cauliflower with a large genetic variety were planted and monitored by five different drone surveys at an altitude of 20 m, using a high resolution 36 Mpix. RGB-camera. In order to minimize intensive radiometric calibration, BRDF effects and eliminate shade, flights were carried out at overcast skies. After photogrammetric image processing, detailed crop height models (CHM) were computed. 10 distinct crop parameters were derived from a combination of the orthophotos, the CHM and additional information. According to the phenological phase a specific set of parameters was developed for every flight. For instance, the position of the individual plants is computed right after the first flight. For the flight prior to harvesting, an algorithm for the head diameter and the curvature of the cauliflower heads was developed. Geometric parameters are generally better suited for automation, because they require less specific ground truth or reference information, than spectrally derived biophysical parameters.