Spectral Reflectance Reconstruction from Red-Green-Blue (RGB) Images for Chlorophyll Content Detection

Chlorophyll is one of the most important pigments in plants, and the measurement of chlorophyll levels enables real-time monitoring of plant growth, which is of great importance to the vegetation monitoring. Compared with the high cost and time-consuming operation of hyperspectral imaging technique, the spectral reflectance reconstruction technique based on RGB images has the advantages of being inexpensive and fast. In this study, using the example of ginkgo leaves, the spectra were reconstructed from red-green-blue (RGB) images taken by smartphones based on a back propagation (BP) neural network and pseudo-inverse method. Based on a BP neural network, the maximum absolute error between the reconstructed spectra and the reference spectra acquired by the hyperspectral camera was less than 0.038. A partial least squares regression (PLSR) prediction model for chlorophyll content estimation was established using the reconstructed spectra. The R² and root mean square error (RMSE) of the validation set were 0.8237 and 1.1895%, respectively, there was a high correlation between predicted and measured values. Compared with the pseudo-inverse method, the maximum absolute error of the reconstructed spectra was reduced by 10.9%, the R² in the chlorophyll prediction results was improved by 12.7%, and the RMSE was reduced by 19.3%. This research showed that reconstructing spectral reflectance based on RGB images can realize real-time measurement of chlorophyll content. It provided a reliable tool for fast and low-cost monitoring of plant physiology and growth conditions.