Precision agriculture (PA) consists of agricultural practice modulation according to intra-field crop variability. Multispectral sensors have standing use in remote sensing, onboard aircraft and satellites for mapping plant cover, biomass, chlorophyll content etc. Absolute reflectance estimation is mandatory to compare data from different weather conditions, time of the day and geographical locations. Real world reflectance estimation requires detailed knowledge of two sensors: one for vegetation radiance imaging; another for measurement of ambient irradiance. If the latter is straightforward with well-tuned Ambient Light Sensors (ALS) and interferential filters, the former requires a prior knowledge of the relationship between irradiance, acquisition parameters and digital number (DN). In the absence of an a priori model of the imaging sensor, its modelling requires different temperatures, irradiances, gains and exposure times to fully explore its behavior. After exploration and modelling of the entire measurement space, a realistic subset of acquisition parameters is chosen for automated calibration on the camera manufacturing line using an integrating sphere and a reference spectrophotometer. Imaging of a reflectance target completes the calibration of the entire system. A targetless workflow has the potential to enable PA for a much larger public. Results from Parrot SEQUOIA will be presented to illustrate the hurdles of multispectral camera characterization and calibration at an industrial scale. The consequences of the difference between the calibration test bench geometry and the on-the-field use will be discussed opening the road to a targetless workflow for multispectral cameras.
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