Machine Learning for Light Sensor Calibration.

ABSTRACT: Sunlight incident on the Earth's atmosphere is essential for life, and it is the driving force of a host of photo-chemical and environmental processes, such as the radiative heating of the atmosphere. We report the description and application of a physical methodology relative to how an ensemble of very low-cost sensors (with a total cost of <$20, less than 0.5% of the cost of the reference sensor) can be used to provide wavelength resolved irradiance spectra with a resolution of 1 nm between 360-780 nm by calibrating against a reference sensor using machine learning. These low-cost sensor ensembles are calibrated using machine learning and can effectively reproduce the observations made by an NIST calibrated reference instrument (Konica Minolta CL-500A with a cost of around USD 6000). The

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC8473444 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature