Hyperspectral estimation of chlorophyll content in grapevine based on feature selection and GA-BP.

Abstract

Leaf chlorophyll content (LCC) is a key indicator for assessing the growth of grapes. Hyperspectral techniques have been applied to LCC research. However, quantitative prediction of grape LCC using this technique remains challenging due to baseline drift, spectral peak overlap, and ambiguity in the sensitive spectral range. To address these issues, two typical crop leaf hyperspectral data were collected to reveal the spectral response characteristics of grape LCC using standardization by variables (SNV) and multiple far scattering correction (MSC) preprocessing variations. The sensitive spectral range is determined by Pearson's algorithm, and sensitive features are further extracted within that range using Extreme Gradient Boosting (XGBoost), Recursive Feature Elimination (RFE), and Principal components analysis (PCA). Comparison of the prediction ability of Random Forest Regression (RFR) algorithm, Support Vector Machine Regression (SVR) model, and Genetic Algorithm-Based Neural Network (GA-BP) on grape LCC based on sensitive features. A SNV-RFE-GA-BP framework for predicting hyperspectral LCC in grapes is proposed, where [Formula: see text]=0.835 and NRMSE = 0.091. The analysis results show that SNV and MSC treatments improve the correlation between spectral reflectance and LCC, and different feature screening methods have a greater impact on the model prediction accuracy. It was shown that SNV-based processed hyperspectral data combined with GA-BP has great potential for efficient chlorophyll monitoring in grapevine. This method provides a new framework theory for constructing a hyperspectral analytical model of grapevine key growth indicators.

Plant phenotyping relevance

ブドウ葉のクロロフィル含量という植物形質を、ハイパースペクトル計測と前処理・特徴選択・機械学習で推定する手法を開発・比較しており、フェノタイピング手法が研究の中心である。

Code and data availability

The supplied blocks contain the abstract, introduction, references, and front/back matter only. No hyperspectral data, phenotype measurements, code, models, or data availability statement for this paper's grapevine LCC analysis appear in the supplied text; all URLs are cited prior work.

No evidence-backed public reproduction asset is currently recorded.