Real-time monitoring of dynamic reflectance using a compact multispectral imaging and machine learning system

Many biological materials exhibit time-dependent reflectance changes, such as fruit oxidation and leaf senescence. We present a novel, low-cost, and portable multispectral imaging solution, aided by machine learning, for real-time monitoring of dynamic reflectance. Built around a miniaturized 16-channel spectral camera (415—809 nm), the system captures wide-field, time-resolved spectral reflectance data without bulky hardware. Experiments on leaf senescence and fruit oxidation demonstrate the ability to capture wavelength-dependent and spatially heterogeneous reflectance variations over long-duration or short-duration. The results highlight the potential of the compact multispectral imaging for non-destructive monitoring application in agriculture, food science, and primary industries.