Prediction chlorophyll content of Zizania latifolia using hyperspectral data and machine learning

Abstract

Chlorophyll content can be indicative of plant physiological activity and then changes in chlorophyll content have been used as a good indicator of disease as well as nutritional and environmental stresses on plants. Chlorophyll content estimation is one of the most applications of hyperspectral remote sensing data. Also, Random Forest (RF) has been applied to assess biochemical properties from remote sensing data; however, an approach integrating with dimensionality reduction techniques has not been fully evaluated. A total of 200 leaves were measured for reflectance and chlorophyll content and then the regression models were generated based on RF with three dimensionality reduction methods including principal component analysis, kernel principal component analysis and independent component analysis. This research clarified that PCA is the best method for dimensionality reduction for estimating chlorophyll content in Zizania Latifolia with a RMSE value of 5.65 ± 0.58 μg cm-2.