Accurate quantitative detection of sodium (Na) content in sorghum roots based on multi-source data fusion of LIBS and HSI.

Abstract

The study of sodium content in plants is crucial for the improvement of saline-alkali soil. Existing metal element detection methods pose challenges because they are complicated and time-consuming. In this study, we propose a quantitative detection model, FusionNet, that integrates Laser-Induced Breakdown Spectroscopy (LIBS) and Near-Infrared Hyperspectral Imaging (NIR-HSI) to realize the detection of Na element content in sorghum roots. To address the small-sample dataset, A Generative Adversarial Network (GAN) was employed to increase the diversity of the samples. The results indicated that data augmentation effectively enhanced the diversity of the original dataset and improved model performance. The modeling results from the FusionNet network achieved R 2 cv of 0.9915 and RMSECV of 0.7418, while R 2 p and RMSEP were 0.9808 and 0.6693. Compared to training with LIBS data alone, FusionNet achieved improvements of 4.94 % in R 2 cv and 5.61 % in R 2 p. This study provides a new method for detecting metal elements in plants.

Plant phenotyping relevance

植物根のNa含量という形質を対象に、LIBSとNIR-HSIを融合した定量検出モデルを開発し、データ拡張と性能評価まで行っており、フェノタイピング手法が中心である。

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