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Improving the Estimation of Apple Leaf Photosynthetic Pigment Content Using Fractional Derivatives and Machine Learning

文献类型： 外文期刊

作者： Cheng, Jinpeng 1 ; Yang, Guijun 2 ; Xu, Weimeng 2 ; Feng, Haikuan 1 ; Han, Shaoyu 2 ; Liu, Miao 2 ; Zhao, Fa 2 ; Zhu, Yaohui 2 ; Zhao, Yu 2 ; Wu, Baoguo 1 ; Yang, Hao 2 ;

作者机构： 1.Beijing Forestry Univ, Sch Informat Sci & Technol, Beijing 100083, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China

3.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

4.Natl Forestry & Grassland Adm, Engn Res Ctr Forestry Oriented Intelligent Inform, Beijing 100083, Peoples R China

关键词： apple leaf; photosynthetic pigment content; fractional derivative; machine learning; hyperspectral data

期刊名称：AGRONOMY-BASEL （ 影响因子：3.949； 五年影响因子：4.117 ）

ISSN：

年卷期： 2022 年 12 卷 7 期

页码：

收录情况： SCI

摘要： As a key functional trait, leaf photosynthetic pigment content (LPPC) plays an important role in the health status monitoring and yield estimation of apples. Hyperspectral features including vegetation indices (VIs) and derivatives are widely used in retrieving vegetation biophysical parameters. The fractional derivative spectral method shows great potential in retrieving LPPC. However, the performance of fractional derivatives and machine learning (ML) for retrieving apple LPPC still needs to be explored. The objective of this study is to test the capacity of using fractional derivative and ML methods to retrieve apple LPPC. Here, the hyperspectral data in the 400-2500 nm domains was used to calculate the fractional derivative order of 0.2-2, and then the sensitive bands were screened through feature dimensionality reduction to train ML to build the LPPC estimation model. Additionally, VIs-based ML methods and empirical regression models were developed to compare with the fractional derivative methods. The results showed that fractional derivative-driven ML methods have higher accuracy than the ML methods driven by the original spectra or vegetation index. The results also showed that the ML methods perform better than empirical regression models. Specifically, the best estimates of chlorophyll content and carotenoid content were achieved using support vector regression (SVR) at the derivative order of 0.2 (R-2 = 0.78) and 0.4 (R-2 = 0.75), respectively. The fractional derivative maintained a good universality in retrieving the LPPC of multiple phenological periods. Therefore, this study highlights that the fractional derivative and ML improved the estimation of apple LPPC.

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