Detection of Diseased Potato Plants with UAV Hyperspectral Imagery

Uncontrolled potato diseases can cause significant yield loss. UAV-based hyperspectral imaging offers a promising method to comprehensively inspect and identify diseased plants across entire fields. This study explored how dimensionality reduction of UAV hyperspectral imagery can enable disease detection with deep learning. Data was collected with the Headwall Nano line-scan sensor, which captures 270 bands over a 400 to 1000nm spectral range. The data was converted into three-band imagery and fed into the YOLOv5s model, which successfully detected the plants infected with blackleg and Potato Virus Y (PVY). The pre-trained model achieved an average [email protected] of 0.85 and an average [email protected] of 0.73 for blackleg detection, as well as an average [email protected] of 0.82 and an average [email protected] of 0.69 for PVY detection, each calculated over ten independent experiments. The results demonstrated the potential of using UAV-based hyperspectral imagery with deep learning techniques for precision agriculture.