Detection of Apple Marssonina Blotch with PLSR, PCA, and LDA Using Outdoor Hyperspectral Imaging

Abstract

In this study, hyperspectral images were used to detect a fungal disease in apple leaves called Marssonina blotch (AMB). Estimation models were built to classify healthy, asymptomatic and symptomatic classes using partial least squares regression (PLSR) , principal component analysis (PCA) , and linear discriminant analysis (LDA) multivariate methods. In general, the LDA estimation model performed the best among the three models in detecting AMB asymptomatic pixels, while all the models were able to detect the symptomatic class. LDA correctly classified asymptomatic pixels and LDA model predicted them with an accuracy of 88. 0%. An accuracy of 91. 4% was achieved as the total classification accuracy. The results from this work indicate the potential of using the LDA estimation model to identify asymptomatic pixels on leaves infected by AMB.