Development of a Portable Spectroradiometer for Assessing the Light Environment in Crop Production

When growing plants in artificial conditions, it is important to control the lighting parameters, both natural and artificial. This study explored the feasibility of creating a low-cost portable spectroradiometer for assessing the light environment in crop production. The multi-channel spectrometer sensors AS7341 and AS7263 and the ESP32 module were selected for this task. An analysis of the problem was conducted, and ways to solve it were identified. Machine learning methods (linear regression and decision trees) were used to determine the light source type, recover the spectrum from sensor readings at individual wavelengths, and estimate the photon flux density. The obtained results were evaluated using the MAE, MRPE, and R2 metrics, resulting in MRPE up to 10% for photon flux higher than 50 µmol m−2 s−1, MAE up to 10 µmol m−2 s−1 for less intensities, R2 of at least 0.96 for almost all cases. It is shown that the developed algorithm achieves acceptable accuracy on various light sources, including those not used during the training process. The research results will be useful in the development of low-cost spectroradiometers for measuring illumination in crop production.