Authors

Abstract

This study focused on three tea cultivars from the Jianghan Plain to construct an inversion model between multispectral features and chlorophyll content in tea leaves. Based on 120 samples across two growth stages, indoor multispectral imaging technology was used to simultaneously acquire leaf multispectral data and SPAD values. Through the analysis of the spectral-chlorophyll response mechanism and the evaluation of feature wavelength autocorrelation, the Iteratively Retained Informative Variables (IRIV) algorithm was integrated for feature selection. An evaluation system consisting of seven machine learning models, including Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR), was established. The results showed that the model combining the adjacent band change rate features selected by IRIV with Multiple Linear Regression (MLR) achieved the optimal inversion accuracy (R²=0.785, RMSE=4.241). Additionally, the vegetation index-MLR combination (R²=0.791, RMSE=4.222) and the mixed feature-LASSO combination (R²=0.773, RMSE=4.403) performed prominently under different feature dimensions. This study provides a feature engineering scheme with strong interpretability and a model optimization path for hyperspectral non-destructive detection of tea physiological parameters.

Abstract in Chinese

本研究以江汉平原的三个茶树品种作为研究对象，构建多光谱特征与茶叶叶片叶绿素含量的反演模型。基于两个生长阶段的120组样本，采用室内多光谱成像技术同步获取叶片多光谱数据及SPAD值，通过光谱-叶绿素响应机制解析与特征波长自相关性评估，融合迭代保留信息变量（IRIV）进行特征筛选，构建包含偏最小二乘回归（PLSR）、支持向量回归（SVR）等七种机器学习模型的评估体系。研究表明：基于IRIV筛选的相邻波段变化率特征结合多元线性回归（MLR）模型反演精度最优（R²=0.785，RMSE=4.241），而植被指数-MLR组合（R²=0.791，RMSE=4.222）及混合特征-LASSO组合（R²=0.773，RMSE=4.403）在不同特征维度下表现突出。本研究为高光谱无损检测茶叶生理参数提供了可解释性强的特征工程方案与模型优化路径。