Harnessing hyperspectral imaging and machine learning to enhance salinity stress detection in canola

Abstract

This study investigates the application of hyperspectral imaging and machine learning techniques for detecting and classifying salinity stress in canola (Brassica napus L.). After analysis of various methods, we employed a ridge classifier model to categorize six classes of salinity, utilizing various spectral bands and vegetation indices across multiple model iterations. Spectral signature analysis revealed significant changes in reflectance patterns for wavelengths exceeding 740 nm, corresponding to the near-infrared (NIR) region. We developed two novel vegetation indices tailored for salinity stress detection, which, when combined with established indices and selected spectral bands, significantly improved classification accuracy. Our sequential model refinement process demonstrated incremental improvements in accuracy, with the final model achieving 82.61 % accuracy on the test set using only 15 features. This represents a substantial reduction from the initial 331 features while maintaining high accuracy. The most effective features primarily spanned wavelengths corresponding to Sentinel-2A bands, with notable exceptions at 405.04 nm and 983.96 nm. Comparison with Sentinel-2 spectral bands revealed that while some important wavelengths align with the satellite sensor’s capabilities, several fall outside its capture range. Notably, our findings suggest that Sentinel-2 bands B1, B5, B6, B7, and B9 may have limited efficacy in identifying salinity stress in canola, highlighting the potential for crop-specific optimization of spectral bands in remote sensing applications. This comprehensive analysis provides insights into the most effective spectral regions and vegetation indices for salinity classification in canola, offering the potential for improved precision agriculture practices. Our findings contribute to the growing body of knowledge on non-invasive crop stress detection and pave the way for future research in hyperspectral imaging applications for sustainable agriculture.

Plant phenotyping relevance

ハイパースペクトル画像と機械学習を用いて、カノーラの塩ストレスを検出・分類する手法を開発・評価しており、植物状態の取得・推定が研究の中心である。

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