1：Experimental Design and Method Selection:

The study used hyperspectral remote sensing to detect peanut leaf spot disease. It involved measuring canopy reflectance spectra, deriving normalized difference spectral indices (NDSI), and performing linear regression analysis to correlate spectral indices with disease index (DI). The reduced sampling method was adopted for systematic quantification of hyperspectral indices.

2：Sample Selection and Data Sources:

Two experiments were conducted in 2017. Experiment 1 used peanut cultivar 'Baisha 1016' (susceptible to leaf spot) and 'Yuhua 15' (resistant) at South China Agricultural University, Guangzhou, China. Different disease intensities were generated using carbendazim fungicide applications. Experiment 2 used 'Baisha 1016' at Jialiao Farm, Zhanjiang City, Guangdong Province, with natural leaf spot occurrence. Disease severity was assessed using a 0-10 scale, and DI was calculated.

3：Experiment 1 used peanut cultivar 'Baisha 1016' (susceptible to leaf spot) and 'Yuhua 15' (resistant) at South China Agricultural University, Guangzhou, China. Different disease intensities were generated using carbendazim fungicide applications. Experiment 2 used 'Baisha 1016' at Jialiao Farm, Zhanjiang City, Guangdong Province, with natural leaf spot occurrence. Disease severity was assessed using a 0-10 scale, and DI was calculated. List of Experimental Equipment and Materials:

3. List of Experimental Equipment and Materials: FieldSpec UV/VNIR spectraradiometer (ASD Inc., Boulder, Colorado, USA) for reflectance measurements, carbendazim fungicide, peanut cultivars, and software (ASD ViewSpecPro, MATLAB, OriginPro 9.0) for data analysis.

4：0) for data analysis. Experimental Procedures and Operational Workflow:

4. Experimental Procedures and Operational Workflow: Canopy reflectance spectra were measured from 325-1075 nm at 3 nm intervals (interpolated to 1 nm) using the spectraradiometer, positioned

5：5 m above the canopy. Measurements were taken on clear days between

00-14:00 h. A panel radiance measurement was used for calibration. For each plot, 20 duplicate measurements were averaged. Disease assessment involved visual scoring of plants, and DI was computed. Data analysis included correlation analysis, regression modeling, and performance evaluation using R2, RMSE, and slope.

6：Data Analysis Methods:

Linear regression analysis was performed to quantify relationships between spectral indices and DI. Statistical significance was evaluated at p=0.05 and 0.001. Root mean square error (RMSE) and R2 were used for model evaluation. Contour maps of R2 values were generated using MATLAB and OriginPro.