AUTHOR=Li Hui , Wan Long , Li Chengsong , Wang Lihong , Zhu Shiping , Chen Xinping , Wang Pei TITLE=Hyperspectal imaging technology for phenotyping iron and boron deficiency in Brassica napus under greenhouse conditions JOURNAL=Frontiers in Plant Science VOLUME=15 YEAR=2024 URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1351301 DOI=10.3389/fpls.2024.1351301 ISSN=1664-462X ABSTRACT=Introduction

The micronutrient deficiency of iron and boron is a common issue affecting the growth of rapeseed (Brassica napus). In this study, a non-destructive diagnosis method for iron and boron deficiency in Brassica napus (genotype: Zhongshuang 11) using hyperspectral imaging technology was established.

Methods

The recognition accuracy was compared using the Fisher Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) recognition models. Recognition results showed that Multiple Scattering Correction (MSC) could be applied for the full band hyperspectral data processing, while the LDA models presented better performance on establishing the leaf iron and boron deficiency symptom recognition than the SVM models.

Results

The recognition accuracy of the training set reached 96.67%, and the recognition rate of the prediction set could be 91.67%. To improve the model accuracy, the Competitive Adaptive Reweighted Sampling algorithm (CARS) was added to construct the MSC-CARS-LDA model. 33 featured wavelengths were selected via CARS. The recognition accuracy of the MSC-CARS-LDA training set was 100%, while the recognition accuracy of the MSC-CARS-LDA prediction set was 95.00%.

Discussion

This study indicates that, it is capable to identify the iron and boron deficiency in rapeseed using hyperspectral imaging technology.