A novel wavelength selection strategy for chlorophyll prediction by MWPLS and GA

The research proposed a novel wavelength selection strategy by the combination of moving window partial least squares (MWPLS) and genetic algorithm (GA) for the chlorophyll content detection of winter wheat canopy using spectroscopy technology. Firstly, the original spectral dataset was pre-processed by wavelet denosing, multiple scatter correction. Then, abnormal data samples were removed by Pauta Criterion and the dataset was divided into modeling set and validation set by SPXY. Finally, the sensitive wavebands were selected using MWPLS method and MWPLS+GA respectively and partial least squares (PLS) models were established for chlorophyll content prediction. For the model established by using all the wavebands in the region of 400-900 nm, its Rc2 and Rv2 were 0.4468 and 0.3821 respectively; its modeling root mean square error (RMSEM) and verification root mean square error (RMSEV) were 2.9057 and 1.7589 respectively. For the model established by using 151 wavebands selected by MWPLS, its Rc2 and Rv2 were 0.6210 and 0.5901 respectively; its RMSEM and RMSEV were 2.4007 and 1.6408 respectively. For the model established by using 36 wavebands selected by MWPLS+GA, its Rc2 and Rv2 were 0.7805 and 0.7497 respectively; its RMSEM and RMSEV were 1.8504 and 1.1315 respectively. The results show that wavelength selection can remove redundant information and improve model performance. The strategy of combining MWPLS with GA has also been proved to work well in selecting sensitive wavebands for chlorophyll content prediction.