Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms.

Abstract

Tea trees are kept in shaded locations to increase their chlorophyll content, which influences green tea quality. Therefore, monitoring change in chlorophyll content under low light conditions is important for managing tea trees and producing high-quality green tea. Hyperspectral remote sensing is one of the most frequently used methods for estimating chlorophyll content. Numerous studies based on data collected under relatively low-stress conditions and many hyperspectral indices and radiative transfer models show that shade-grown tea performs poorly. The performance of four machine learning algorithms-random forest, support vector machine, deep belief nets, and kernel-based extreme learning machine (KELM)-in evaluating data collected from tea leaves cultivated under different shade treatments was tested. KELM performed best with a root-mean-square error of 8.94 ± 3.05 μg cm -2 and performance to deviation values from 1.70 to 8.04 for the test data. These results suggest that a combination of hyperspectral reflectance and KELM has the potential to trace changes in the chlorophyll content of shaded tea leaves.

Plant phenotyping relevance

茶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反射と機械学習で非破壊推定する手法が研究の中心であり、複数アルゴリズムの性能比較・評価も行っている。

Code and data availability

The paper reports tea leaf hyperspectral reflectance and chlorophyll measurements analyzed with RF, SVM, DBN, and KELM in R/MATLAB, but provides no public deposit of its phenotype dataset, spectral data, or author analysis code. All URLs mentioned are generic tools (R, e1071, rBayesianOptimization) or third-party ELM/K

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