Feature Vector Based Analysis of Hyperspectral Crop Reflectance Data for Discrimination and Quantification of Fungal Disease Severity in Wheat

The impact of plant pathological stress on crop reflectance can be measured both in broad-band vegetation indices and in narrow or local characteristics of the reflectance spectra. This work is concerned with using the whole spectra in the objective examination of how different parts of the spectrum contribute in describing disease severity in wheat. A hyperspectral reflectance spectrum was considered as a mixed signal, i.e. the integration of the effects of all active objects in the investigated area. Independent component analysis (ICA) was used to blindly separate mixed statistically independent signals. Principal component analysis (PCA) was also used to extract interesting components. The ICA or PCA results had then to be interpreted efficiently. This was achieved by using a technique called feature-vector-based analysis (FVBA), which produces a number of ‘component–feature vector’ pairs, which represent the spectral signatures and the corresponding weighting coefficients of the different constituting source signals. These weighting coefficients were proportional to field assessments of fungal disease severity in a spring wheat crop, in percentage necrosis of leaf area, and high correlations were shown. Two effects of increased disease severity were observed: (1) a flattening of the green reflectance peak together with a general decrease in reflectance in the near-infrared region and (2) a decrease of the shoulder of the near-infrared reflectance plateau together with a general increase in the visible region between 550 and 750 nm.