Estimating chlorophyll content of Zizania latifolia with hyperspectral data and random forest

Year 2022, Volume: 7 Issue: 3, 221 - 228, 15.10.2022

Adenan Yandra Nofrizal , Rei Sonobe Yamashita Hıroto Akio Morita Takashi Ikka

https://doi.org/10.26833/ijeg.953188

Cited By: 7

Abstract

The amount of chlorophyll in a plant useful to indicate its 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. The aim of this study is to evaluate dimensionality reduction for estimating chlorophyll contents from hyperspectral reflectance. Random Forest (RF) has been applied to assess biochemical properties such as chlorophyll content from remote sensing data; however, an approach integrating with dimensionality reduction techniques has not been fully evaluated. A total of 200 Zizania latifolia leaves with 5 treatments from Shizuoka University field were measured for reflectance and chlorophyll content. 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.  

Keywords

Dimensionality reduction , Zizania latifolia , Hyperspectral , Machine learning

Thanks

We thank the members of the Laboratory of Plant Functional Physiology and the Laboratory of Macroecology, Shizuoka University, for their support during both fieldwork and laboratory analyses

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