Detection of Fire Blight disease in pear trees by hyperspectral data

Rapid and early detection of Fire Blight as the most destructive bacterial disease of apple and pear trees is very important to avoid product loss. The objective of this research was to evaluate the usefulness of visible near-infrared spectrometry for early detection of Fire Blight. Three kinds of samples were selected: healthy leaves (H) from healthy trees and symptomatic (S) and non-symptomatic diseased (MS) leaves from infected trees. For spectral analysis, different preprocessing and processing techniques were carried out. Linear discriminant analysis, quadratic discriminant analysis, Mahalanobis discriminant analysis, soft independent modeling of class analogy (SIMCA) and partial least square-discrimination analysis were applied as classification techniques. Laboratory test by selective culture method was used to detect bacteria. Based on analyses, hyperspectral wavelengths for detection of H, MS and S leaves were obtained. SIMCA proved to be the strongest among all classifiers to discriminate healthy leaves from diseased leaves. The results indicated that structure intensive pigment index and modified simple ratio were sensitive to discriminate H-S, H-MS and S-MS leaves. Randomized difference vegetation index showed potential to classify H-S and S-MS samples. Anthocyanin reflectance index showed potential to discriminate H-MS samples. Finally, modified triangular vegetation index1 and modified chlorophyll absorption ratio index1 were identified and considered as spectral indices to discriminate S-MS samples. Based on these results, this technique is reliable for detecting non-symptomatic diseased leaves and is capable of early detection of Fire Blight before spreading.