Detection of apple Marssonina blotch disease using hyperspectral imaging

 Abstract. Apple Marssonina blotch (AMB) is one of the most known devastating apple diseases and it has caused huge economic losses to countries like Japan, India, and Korea. It is a fungal disease that mainly affects the leaves of apple trees and causes premature defoliation, which in turn results in low quality and quantity of harvested apples. Technologies that can efficiently detect the disease at its early stage could help growers apply timely control measures to contain the spread of the disease. In this work, the use of hyperspectral imaging and spectroradiometer measurements were examined for the early diagnosis of the disease. Anthocyanin reflectance index 1 (ARI1), modified triangular vegetation index (MTVI), and red edge position (REP) vegetation indices and spectral reflectance data were used as features in building quadratic discriminant and support vector machine (SVM) classifiers. An endmember extraction algorithm, known as sequential maximum angle convex cone (SMACC), was used to create hyperspectral endmembers based on the severity of the disease. Both spectroradiometer and hyperspectral imaging systems worked well in distinguishing healthy samples from diseased samples. The highest classification accuracies achieved in this work for both healthy and early stage AMB diseased classes were 97.7% and 99.2%, respectively.