Unsupervised hyperspectral band selection for apple Marssonina blotch detection

Abstract Apple Marssonina blotch (AMB) is a severe fungal disease that has been plaguing top apple producing countries in the world since it was first found in Japan in 1907. The disease causes premature defoliation and eventually leads to fruit shrinkage and reduction of starch content. AMB has a long latency period ranging from two to five weeks and at its early symptomatic stage, the disease develops symptoms similar to other apple blotch-like diseases, thus making it difficult to detect using only visible information. Hyperspectral imagery was investigated in this study for the detection of different stages of AMB. While hyperspectral images contain a wealth of information that can help distinguish between similar-looking objects, they also contain a large amount of redundancy. An unsupervised feature selection method called orthogonal subspace projection (OSP) was used to perform feature selection and redundancy reduction simultaneously. Ten optimal spectral bands were selected using the algorithm, with six out the selected bands within the same near-infrared spectral region. These bands served as input features for three classifiers—ensemble bagged, decision tree and weighted k-nearest neighbor. The selected bands and classifiers achieved overall accuracy ranging from 71.3% to 84.3%, thus indicating the feasibility of using the OSP feature selection method for reducing the size of hyperspectral data and designing a multispectral imaging system for detecting various AMB disease stages.