Improvement on chlorophyll fluorescence parameters detection of potato leaf based on wavelet cluster analysis

Abstract

Chlorophyll fluorescence parameters (CFPs), especially maximum photosynthetic efficiency of optical system II (Fv/Fm), are the intrinsic photosynthesis probes of crop stress and photosynthetic function. Hyperspectral image (HSI) offers a rapid alternative to traditional pulse amplitude modulation for Fv/Fm, but selecting the uninformative wavelengths reduce accuracy. To address this, a Wavelet Cluster (WCL) method based on Continuous wavelet transform (CWT) was proposed to enhance sensitive wavelengths extraction. Spectral data was preprocessed by Savizky-Golay smoothing (SG) and Multiple scattering correction (MSC), followed by CWT decomposition with bior3.3, gaus4 and meyr wavelet functions to form WCL. Sensitive wavelet coefficients (WCs) were selected using Monte Carlo uninformative variable elimination (MC-UVE), and the Partial Least Squares Regression (PLSR) and Random Forest (RF) modeling methods were established. The results showed that (1) the reflectance spectrum decreased with the increase of Fv/Fm in potato leaf. (2) WCL effectively captured chlorophyll fluorescence spectral features. (3) The WCL-RF model outperformed WCL-PLSR, with a calibration set Rc2 of 0.75, RMSEc of 0.0209, a prediction set Rp2 of 0.73, RMSEp of 0.0225, respectively. This study demonstrates the potential of WCL for accurate Fv/Fm detection, and supports for potato canopy photosynthetic activity assessment.

Plant phenotyping relevance

ジャガイモ葉のクロロフィル蛍光指標Fv/Fmという植物生理形質を、ハイパースペクトル画像と新規のウェーブレットクラスタ解析で推定し、モデル性能も評価しているため、フェノタイピング手法が中心である。

Code and data availability

公開状態または取得可能な本文経路を確認できませんでした。

No evidence-backed public reproduction asset is currently recorded.