Laurel wilt (Lw) is a very destructive disease and poses a serious threat to the commercial production of avocado in Florida, USA. External symptoms of Lw are similar to those that are caused by other diseases and disorders. A rapid technique to distinguish Lw infected avocado from healthy trees and trees with other abiotic stressors is presented in this paper. A novel method was developed to analyze data from hyperspectral data using finite difference approximation (FDA) and bivariate correlation (BC) to discriminate Lw, Nitrogen (N), and Iron (Fe) deficiencies from healthy avocado plants. Several combinatorial methods were used in preprocessing the data, such as standard normal transformation of data, smoothing of the data, and polynomial fit. The FDA technique was derived using a Taylor Polynomial finite difference approximation. This FDA accentuates inflection points in the spectrum. These, in turn, reveal variance in the data that can be used to identify spectral signature associated with healthy and diseased states. By statistical correlation using the bivariate correlation coefficient of these enhanced spectral patterns, an algorithm (FDA-BC) for distinguishing Lw avocado leaves from all other categories of healthy or mineral deficient avocado leaves is achieved with an overall accuracy of 100%.

This chapter introduces several feature extraction techniques (FETs) and machine learning algorithms (MLA) that are useful for pattern recognition in hyperspectral data analysis (HDA). This chapter provides a handbook of the most popular FETs that have proven successful. Machine learning algorithms (MLA) for use with HDA are becoming prevalent in pattern recognition literature. Several of these algorithms are explained in detail to provide the user with insights into applying these for pattern recognition. Unsupervised learning applications are useful when the system is provided with the correct set of independent variables. Various forms of linear regression assay adequately solve hyperspectral pattern resolution for identifying phenotypes. K-means is an unsupervised learning algorithm that is used for systematically dividing a dataset into K number of pattern groups. Supervised and unsupervised neural networks (NNs) are used to discern patterns in hyperspectral data with features as inputs and in large datasets where little a priori knowledge is applied. Other supervised machine learning procedures derive valuable feature detectors and descriptors through support vector machine. Several methods using reduced sets for extracting patterns from hyperspectral data are shown by discretized numerical techniques and transformation processes. The accuracy of these methods and their usefulness is generally assessed.

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