DefoliationCAM: An Edge-AI System for Real-Time Insect Defoliation Severity Assessment in Grapevines
Insect-induced defoliation threatens grapevine health and yield, yet severity assessment relies on subjective manual scouting.
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Key points
- We developed DefoliationCAM (D-CAM), a solar-powered edge AI system that estimates canopy defoliation and transmits results via LoRa to a web dashboard.
- The pipeline uses YOLOX-Nano to localize grapevine cordons in 4K images and a MobileNet-based classifier with lightweight channel attention to classify 320 x 320 pixel patches.
- Patch predictions were aggregated into a canopy-level Non-linear Global Defoliation Severity (NGDS) score and validated against an entomologist's ratings of 86 held-out canopy images.
- D-CAM supports frequent, objective canopy damage assessment for targeted scouting and timely pest-management decisions.
Sources (1)
- [1]DefoliationCAM: An Edge-AI System for Real-Time Insect Defoliation Severity Assessment in GrapevinesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 08:12 PM
Insect-induced defoliation threatens grapevine health and yield, yet severity assessment relies on subjective manual scouting.
We developed DefoliationCAM (D-CAM), a solar-powered edge AI system that estimates canopy defoliation and transmits results via LoRa to a web dashboard.
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