Indirect quantitative analysis of soluble solid content in citrus by the leaves using hyperspectral imaging combined with machine learning.

Abstract

Due to the effect of bagging on fruit growth, non-destructive and in situ soluble solid content (SSC) in citrus detection remains a challenge. In this work, a new method for accurately quantifying SSC in citrus using hyperspectral imaging of citrus leaves was proposed. Sixty-five Ehime Kashi No. 28 citruses with surrounding leaves picked at two different times were picked for the experiment. Using the principal components analysis combined with Gaussian process regression model, the correlation coefficients of prediction-real value by citrus and its leaves in cross-validation were 0.972 and 0.986, respectively. In addition, the relationship between citrus leaves and SSC content was further explored, and the possible relationship between chlorophyll in leaves and SSC of citrus was analyzed. Comparing the quantitative analysis results by citrus and its leaves, the results show that the proposed method is a non-destructive and reliable method for determining the SSC by citrus leaves and has broad application prospects in indirect detection of citrus.

Plant phenotyping relevance

柑橘葉のハイパースペクトル画像と機械学習により果実SSCを非破壊推定する手法の提案・評価が中心であり、植物形質の取得手法に該当する。

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