Problem-Specific Optimized Multispectral Sensing for Improved Quantification of Plant Biochemical Constituents

Multispectral cameras are gaining popularity in the field of smart farming and plant phenotyping. They are more cost-effective than hyperspectral cameras and frame-based imaging allows easier operation and processing, while at the same time the limited spectral resolution still allows the retrieval of relevant information about the plant status. Typically, multispectral cameras are available with equidistant channels spanning a defined spectral range. We propose a design approach based on Bayesian optimization to define problem-specific spectral channels for multispectral cameras in the plant phenotyping domain. Compared to established wavelength selection algorithms, our approach considers physical constraints of optical filters such as feasible filter function shape and width. The filter functions are optimized and tested to predict plant pigment concentration and Equivalent Water Thickness of simulated spectra generated with the PROSPECT-D leaf radiative transfer model. Problem-specific multispectral camera design could potentially enhance prediction performance of automated plant status monitoring.