Detection of chlorophyll content based on optical properties of maize leaves.

The chlorophyll content reflects plants' photosynthetic capacity, growth stage, and nitrogen status. Maize is one of the three widely planted gain crops in the world. In order to offer useful information for the development of chlorophyll content detectors of maize leaves, a single integrating sphere system was used to measure the transmittance and reflectance spectra of maize leaves over the wavelength range of 500-950 nm. The linear relationships of transmittance and reflectance with chlorophyll content were investigated. The feature wavelengths (FWs) sensitive to chlorophyll content were extracted from the full transmittance and reflectance spectra using the successive projections algorithm (SPA). The partial least squares regression (PLSR) models for predicting the chlorophyll content were established using the full spectra and extracted FWs. The results showed that there were obvious linear relationships between transmittance and reflectance with chlorophyll content of maize leaves and the best linear relationships were found at 709 nm and 714 nm, respectively, with the linear correlation coefficients of 0.801 and 0.696, and the root-mean-squares error (RMSEP) of 0.321 mg·g and 0.405 mg·g, respectively. Eight and 6 FWs were extracted from the transmittance and reflectance spectra, respectively. The PLSR model established using the selected FWs from transmittance spectra had better prediction performance with RMSEP of 0.208 mg·g than using full transmittance spectra. The built PLSR models using the full reflectance spectra and extracted FWs had poor robustness. This research offers some theoretical basis for developing a maize leaf chlorophyll content detector based on transmittance or reflectance.