Canopy height estimation using drone-based RGB images

Abstract

Canopy height is an important crop biophysical parameter. It provides information about the crop growth as well as act as an input parameter for biomass and crop yield models. Considering the importance of this parameter, a novel semi-automatic canopy height estimation model has been developed which can work with both georeferenced or non-georeferenced top-of-canopy aerial images. The model employs a Structure-from-Motion algorithm followed by dense point cloud reconstruction and polygon triangulation to obtain polygon meshes which are used for height estimation. The process has been tested on drone-based data collected from a maize crop over the 2018-19 Rabi season from a semi-arid area in central-south India. The ground truth canopy height was measured by manually measuring height of plants using a meter scale. The ground elevation has been modelled using a linear best fit plane and the estimated canopy height was found to have the best R2 value of 0.85 and RMSE values of 14.17 cm.

Keywords

• Polygon meshes
• Drone-based imaging
• Precision agriculture
• Slope map
• Computational geometry
• Remote sensing