Optimal-Band Analysis for Chlorophyll Quantification in Rice Leaves Using a Custom Hyperspectral Imaging System.

Abstract

Hyperspectral imaging (HSI) is a promising tool in chlorophyll quantification, providing a non-invasive method to collect important information for effective crop management. HSI contributes to food security solutions by optimising crop yields. In this study, we presented a custom HSI system specifically designed to provide a quantitative analysis of leaf chlorophyll content (LCC). To ensure precise estimation, significant wavelengths were identified using optimal-band analysis. Our research was centred on two sets of 120 leaf samples sourced from Thailand's unique Chaew Khing rice variant. The samples were subjected to (i) an analytical LCC assessment and (ii) HSI imaging for spectral reflectance data capture. A linear regression comparison of these datasets revealed that the green (575 ± 2 nm) and near-infrared (788 ± 2 nm) bands were the most outstanding performers. Notably, the green normalised difference vegetation index (GNDVI) was the most reliable during cross-validation (R2=0.78 and RMSE = 2.4 µg∙cm -2 ), outperforming other examined vegetable indices (VIs), such as the simple ratio (RED/GREEN) and the chlorophyll index. The potential development of a streamlined sensor dependent only on these two wavelengths is a significant outcome of identifying these two optimal bands. This innovation can be seamlessly integrated into farming landscapes or attached to UAVs, allowing real-time monitoring and rapid, targeted N management interventions.

Plant phenotyping relevance

イネ葉のクロロフィル含量という植物形質を推定するカスタムハイパースペクトル画像法を開発・検証し、最適波長と交差検証性能を評価しているため、フェノタイピング手法が中心である。

Code and data availability

The article describes custom hyperspectral imaging of 120 rice leaf samples and regression analysis, but no public dataset, image, code, or model deposit is provided. The Data Availability Statement only states the data are in the article itself, with no authors' URL or repository identifier.

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