The invention relates to the technical field of
remote sensing monitoring, in particular to a
pine wood nematode disease identification method and
system based on an unmanned aerial vehicle image, and the method comprises the following steps: obtaining a
pine forest multispectral image through an unmanned aerial vehicle, extracting edge pixels through a Canny
algorithm, decomposing a direction to generate a sequence, calculating a gradient offset, counting curvature
mutation points, and extracting curvature characteristic parameters. The method comprises the following steps: removing abnormal edge segments, performing
DBSCAN clustering to obtain candidate areas, constructing a radial channel to collect gray values, generating gray attenuation features, extracting a three-dimensional feature space, and performing K-means clustering to output a recognition result graph. According to the method, multi-
spectral image gradient
decomposition is combined with curvature fluctuation characteristic analysis,
disease spot microscopic
distortion is accurately captured, non-
pathological interference is inhibited through curvature
mutation points and density clustering, three-dimensional characteristics are constructed through a radial gray attenuation main trend, gray identification is enhanced through direction offset analysis, experience dependence is reduced through multi-threshold screening, and the method is suitable for large-scale popularization and application. The dynamic self-matching framework improves the robustness of forest
region detection, and realizes the autonomous recognition of
disease spots.