Abstract
•A feature extraction method for identifying plant diseases was developed.•Karhunen-Loeve Expansion was used to identify patterns in spectral data.•This method can define a reduced spectral dataset for early-stage disease detection.•It can be used in classical and deep-learning methods for classifying diseases.
This study introduces a novel method using reduced datasets for extracting plant reflectance signatures. Plant reflectance data from hyperspectral imaging sensors were used to develop biomarker signatures for plant identification and early disease detection purposes. The Karhunen-Loeve Expansion (KLE) of spectral reflectance data, taken from healthy and diseased plants (two case studies of citrus canker in citrus and Laurel wilt in avocado), were used to identify a basis set of functions that represents the distribution of the reflected signal energy. By spectral decomposition, the eigenvalues were related to the KLE basis set. The eigenvalues can be used to identify the KLE eigenvectors, which comprise the highest variation in the data. These components can be interpreted as the weighted variables which carry with them most of the information on the reflectance spectrum of the plant. From indications presented by this multivariate KLE analysis, a frequency reconstruction was adapted to convert the eigenvector information to a wave function. This reconstruction via KLE and frequency transformation formed the signature identification process (i.e., unique biomarker). These frequency spectra can be used as average signature reflectance patterns for plant identification, classification, and disease biomarkers. Defining these spectral identification biomarkers or signatures is purposeful since it could lead to less invasive classification and disease diagnostics techniques. The techniques used to determine these reflectance spectra require a unique spectrum reconstruction method, using Fourier transform methods and KLE for data reduction.
