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 correlation coefficient between the reference sensor and the calibrated low-cost sensor ensemble has been optimized to have R2> 0.99. Both the circuits used and the code have been made publicly available. By accurately calibrating the low-cost sensors, we are able to distribute a large number of low-cost sensors in a neighborhood scale area. It provides unprecedented spatial and temporal insights into the micro-scale variability of the wavelength resolved irradiance, which is relevant for air quality, environmental and agronomy applications.