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

Area of Science:

• Agricultural Science
• Remote Sensing
• Plant Physiology

Background:

• Hyperspectral imaging (HSI) offers non-invasive crop monitoring for better management.
• Accurate leaf chlorophyll content (LCC) is vital for optimizing crop yields and food security.

Purpose of the Study:

• To develop a custom HSI system for quantitative LCC analysis.
• To identify optimal wavelengths for precise chlorophyll estimation in rice.
• To assess the efficacy of identified bands and vegetation indices for LCC quantification.

Main Methods:

• Collected spectral reflectance data from 120 Chaew Khing rice leaf samples using a custom HSI system.
• Performed analytical LCC assessment for calibration and validation.
• Utilized optimal-band analysis and linear regression to identify significant wavelengths.
• Evaluated various vegetation indices (VIs), including GNDVI, for LCC estimation.

Main Results:

• Green (575 ± 2 nm) and near-infrared (788 ± 2 nm) bands showed the highest correlation with LCC.
• The green normalized difference vegetation index (GNDVI) demonstrated the highest reliability (R²=0.78, RMSE = 2.4 µg∙cm⁻²).
• Identified optimal bands outperformed other tested VIs in cross-validation.

Conclusions:

• The study successfully identified optimal wavelengths for accurate LCC quantification using HSI.
• A streamlined sensor utilizing these two bands could enable real-time monitoring.
• This technology supports targeted nutrient management, potentially enhancing crop yields and food security.