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Spatio-temporal-Spectral Environment Modeling using a UAV
The goal of this thesis is to use a Micro Air Vehicle equipped with inertial sensors, a high resolution colour camera and lower resolution multispectral camera to collect and measure field parameters which will enable an analysis of crop health, determination of variations within the field in space
This thesis will be performed within the framework of the European project Flourish. The aim of the project is to develop the underlying science and technologies required to use a combination of aerial and ground vehicles to enable precision agriculture in a cost effective and user friendly manner.
The goal of this thesis is to use a Micro Air Vehicle equipped with inertial sensors, a high resolution colour camera and lower resolution multispectral camera to collect and measure field parameters which will enable an analysis of crop health, determination of variations within the field in space and time and validation of a set of spectral indices for the given sensor system and ground resolution to remotely detect crop status, thus laying the groundwork for autonomous precision farm management and regulating farm inputs.
This thesis will be performed within the framework of the European project Flourish. The aim of the project is to develop the underlying science and technologies required to use a combination of aerial and ground vehicles to enable precision agriculture in a cost effective and user friendly manner. The goal of this thesis is to use a Micro Air Vehicle equipped with inertial sensors, a high resolution colour camera and lower resolution multispectral camera to collect and measure field parameters which will enable an analysis of crop health, determination of variations within the field in space and time and validation of a set of spectral indices for the given sensor system and ground resolution to remotely detect crop status, thus laying the groundwork for autonomous precision farm management and regulating farm inputs.
● Read about the given agricultural field parameters to be measured.
● Literature review on trajectory based camera extrinsic calibration.
● Use data collected during Spring 2016 - 3D maps generated using commercial
software along with pose estimates from the UAV to estimate the transformation matrix between the colour and multispectral camera to project the multispectral data onto the 3D point cloud.
● Determine other field parameters such as average crop height, canopy cover, leaf area index, above ground biomass etc. using the multispectral point clouds.
● Collect the estimated parameters in a spatio-temporal database, compare results with provided ground truth data and analyse the discrepancies.
● Read about the given agricultural field parameters to be measured.
● Literature review on trajectory based camera extrinsic calibration.
● Use data collected during Spring 2016 - 3D maps generated using commercial software along with pose estimates from the UAV to estimate the transformation matrix between the colour and multispectral camera to project the multispectral data onto the 3D point cloud.
● Determine other field parameters such as average crop height, canopy cover, leaf area index, above ground biomass etc. using the multispectral point clouds.
● Collect the estimated parameters in a spatio-temporal database, compare results with provided ground truth data and analyse the discrepancies.
● Prior programming experience (C++/Python/Matlab) with non trivial projects is required.
● Familiarity with ROS, Pix4D is beneficial.
● Some experience with R/C flying (fixed wing or multicopters) is beneficial.
● Prior programming experience (C++/Python/Matlab) with non trivial projects is required.
● Familiarity with ROS, Pix4D is beneficial.
● Some experience with R/C flying (fixed wing or multicopters) is beneficial.