一种早期松材线虫病植株的检测方法
By acquiring early and late remote sensing image sets and combining hyperspectral reconstruction networks and support vector machine methods, an early pine wilt disease detection model was established. This solved the problem of low-cost, large-scale detection of early pine wilt disease plants in existing technologies, achieving low-cost and accurate detection results.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SOUTH CHINA AGRICULTURAL UNIVERSITY
- Filing Date
- 2024-01-10
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies make it difficult to detect early pine wilt disease on a large scale at low cost. Furthermore, existing methods such as chemical detection are time-consuming and labor-intensive, hyperspectral remote sensing images are expensive, and RGB remote sensing images cannot identify early pine wilt disease.
By acquiring early and late remote sensing image sets, RGB images were collected using a ground-following flight method, and solar illuminance was collected simultaneously. Image segmentation and preprocessing were performed, and a detection model for early pine wilt disease was established by combining hyperspectral reconstruction network and support vector machine methods. Spectral reflectance curves were extracted for classification.
It enables low-cost and accurate detection of early-stage pine wilt disease in plants, reducing detection costs and facilitating large-scale application.
Smart Images
Figure CN117994649B_ABST