.png){kind=icon}
.png){kind=icon}
Return
Deep spatial-spectral fusion of UAV RGB and hyperspectral imagery for potato plant disease detection
.png){kind=icon}
OA
AI
T
M
G
L
DOI：10.1016/j.jag.2026.105486.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• Constructed paired UAV RGB and hyperspectral dataset with plant-level annotations. • Proposed deep learning framework (S2-PDD) fusing multi-modal data for potato disease detection. • Combining RGB and vegetation indices improved mAP by 7% over single modality. • PCA-only models showed the lowest detection performance. • Potato virus Y (PVY) detection was more accurate than blackleg disease.
Keywords:
Vegetation index
Early fusion
Middle fusion
Disease detection
Plant-level
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.6
Papers:
5.1K
Citations:
2.4W
Organization
Cited Papers