[Multifractal Analysis of Rapeseed Spectrum for Chlorophyll Diagnosis Modeling]

One of the most important topics in crop information science is how to make use of the crop’s information for non-destructive nutrient diagnosis which can be solved with spectrum analysis. The canopy’s spectrum feature is a key indicator to describe the nutritional status for the rapeseeds. The original spectrum is to be disturbed with external factors such as environment and climate; however, it is difficult to be directly used for rapeseed biomass diagnosis due to its huge fluctuation. However, the multifractal feature of the spectra remains stable relatively. In order to study the relationship between the canopy’s spectrum of the rapeseed and its chlorophyll, based on the multifractal theory, a quantitative model of chlorophyll prediction and a qualitative model of planting pattern identification were proposed in this paper to study the high oleic acid rapeseed samples in 24 transplanting regions and 24 direct planting regions. At first, the generalized Hurst exponent and mass exponents together with other relevant multifractal parameters of the spectra were extracted with popular multifractal detrended fluctuation analysis (MF-DFA) in different six considered wavelength ranges. It shows that all of them possess representative multifractal nature. However, there are some differences of the multifractal characteristics between the two kinds of regions with different planting pattern in some bands. In addition, by correlation analysis and detection between the multifractal parameters of the spectra and the SPAD values in six considered ranges of bands, it demonstrates that there is some difference of the effective information content in the different ranges of bands. In the quantitative model of chlorophyll prediction, for each groups of samples in transplanting regions and direct planting regions and mixed together in each significant bands, a selected multifractal parameter was used to establish the univariate model for predicting the rapeseed leaf’s SPAD values, respectively. The results of all the relative root mean square errors are small than 5%. Finally, the qualitative model was proposed to distinguish the samples by the two planting pattern. Youden index, as the identification accuracy was calculated for the six considered ranges of bands by the Fisher’s linear discriminant analysis. The best Youden index is 0.902 5 and the corresponding band range is 350~1 350 nm. The significant work provides a theoretical and practical method for predicting rapeseed leaf’s SPAD and also provides effective way to find the sensitive bands of the spectra for identification diagnosis.