Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery

TLDR

The study evaluates narrow‑band spectral vegetation indices derived from EO‑1 Hyperion imagery to discriminate sugarcane affected by orange rust disease. Forty SVIs were generated from a Hyperion image of Mackay, Queensland, and discriminant function analysis selected the most correlated indices, whose classification accuracy was then assessed. Hyperion imagery successfully detected orange rust, with indices combining VNIR and the 1660‑nm moisture band—especially the new DWSI indices—showing the highest discriminative performance.

Abstract

This Letter evaluates several narrow-band indices from EO-1 Hyperion imagery in discriminating sugarcane areas affected by 'orange rust' (Puccinia kuehnii) disease. Forty spectral vegetation indices (SVIs), focusing on bands related to leaf pigments, leaf internal structure, and leaf water content, were generated from an image acquired over Mackay, Queensland, Australia. Discriminant function analysis was used to select an optimum set of indices based on their correlations with the discriminant function. The predictive ability of each index was also assessed based on the accuracy of classification. Results demonstrated that Hyperion imagery can be used to detect orange rust disease in sugarcane crops. While some indices that only used visible near-infrared (VNIR) bands (e.g. SIPI and R800/R680) offer separability, the combination of VNIR bands with the moisture-sensitive band (1660 nm) yielded increased separability of rust-affected areas. The newly formulated 'Disease–Water Stress Indices' (DWSI-1=R800/R1660; DSWI-2=R1660/R550; DWSI-5=(R800+R550)/(R1660+R680)) produced the largest correlations, indicating their superior ability to discriminate sugarcane areas affected by orange rust disease.