Hyperspectral Imaging with Machine Learning Approaches for Assessing Soluble Solids Content of Tribute Citru.

Abstract

Tribute Citru is a natural citrus hybrid with plenty of vitamins and nutrients. Fruits' soluble solids content (SSC) is a critical quality index. This study used hyperspectral imaging at two spectral ranges (400-1000 nm and 900-1700 nm) to determine SSC in Tribute Citru. Partial least squares regression (PLSR) and support vector regression (SVR) models were established in order to determine SSC using the spectral information of the calyx and blossom ends. The average spectra of both ends as well as their fusion was studied. The successive projections algorithm (SPA) and the correlation coefficient analysis (CCA) were used to examine the differences in characteristic wavelengths between the two ends. Most models achieved performances with the correlation coefficient of the training, validation, and testing sets over 0.6. Results showed that differences in the performances among the models using the one-sided and two-sided spectral information. No particular regulation could be found for the differences in model performances and characteristic wavelengths. The results illustrated that the sampling side was an influencing factor but not the determinant factor for SSC determination. These results would help with the development of real-world applications for citrus quality inspection without concerning the sampling sides and the spectral ranges.

Plant phenotyping relevance

柑橘果実の可溶性固形分という植物器官の形質を、ハイパースペクトル画像と機械学習で推定する方法を開発・比較しており、表現型取得・推定が中心である。

Code and data availability

The paper's hyperspectral images, extracted spectra, and SSC reference values are not publicly deposited; the Data Availability Statement says they can only be requested from the corresponding author by email. No author code, models, or public URLs are provided.

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