Exploring the impact of lenticels on the detection of soluble solids content in apples and pears using hyperspectral imaging and one-dimensional convolutional neural networks.

Abstract

In this work, the effect of lenticels on the predictive performance of apple and pear soluble solids content (SSC) models developed based on hyperspectral imaging (HSI) at 380-1010 nm was investigated for the first time. Variations in the spectral properties of lenticels, pericarp, and combined lenticels and pericarp regions of interest (ROI) were analyzed using two-dimensional correlation spectroscopy method (2D-COS), factor discriminant analysis (FDA) and principal component analysis (PCA). Partial least squares regression (PLSR) was performed to develop calibration models of SSC for each ROI separately. Furthermore, variable selection algorithm and one-dimensional convolutional neural network (1D-CNN) were utilized to simplify and improve the model prediction capability. The results showed that the spectral properties of lenticels and pericarp vary considerably, while PCA could highlight the distribution of lenticels. The spectral measurement location has a significant effect on the SSC prediction accuracy. The models can be kept robust when the data sources for the prediction and calibration sets are the same. Specifically, for apple fruit, the SPA-1D-CNN achieved the best model performance with R c 2 of 0.845 and R p 2 of 0.808, respectively. For pear fruit, the best model is the CARS-1D-CNN model with Rc 2 of 0.887 and Rp 2 = 0.762. This study demonstrated that lenticels have a significant effect on model prediction performance and the 1D-CNN could be an alternative to conventional PLSR method.

Plant phenotyping relevance

ハイパースペクトル画像からリンゴ・ナシ果実の可溶性固形分含量を推定するモデルを開発・比較しており、形質取得・推定手法が研究の中心である。

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