Dissertation study 1 · Machine vision
Real-time weed perception in complex orchard rows
An attention-enhanced vision framework that detects, segments, tracks, localizes, and estimates the spatial density of intra-row weeds from a side-view camera.
Research challenge
Low branches, trunks, irrigation lines, shadows, and occlusion make orchard weeds difficult to see consistently.
Engineering approach
- 01Build and annotate a multi-species orchard weed dataset.
- 02Enhance YOLOv7 segmentation with a convolutional block attention module.
- 03Preserve weed identities across frames with DeepSORT and filtering.
- 04Convert masks into a 20 × 20 pixel grid for localized density estimation.
Systems in action
Weed tracking and real-time perception
The tracking sequence shows weed identities across orchard frames. The interface recording demonstrates how detections, size estimates, and nozzle assignments are brought together during field operation.
Detection masks, tracking IDs, and spatial grid overlays demonstrate real-time tracking and identity-preservation performance under branches, trunks, shadows, and occlusion.
Live orchard imagery with weed detections, tracking IDs, spatial grids, size estimates, nozzle assignments, and spray activity logs shows how perception supports targeted spray decisions.
Why it matters