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DefoliationCAM: An Edge-AI System for Real-Time Insect Defoliation Severity Assessment in Grapevines — arXiv:2610.20879

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.20879
License (as archived): CC0-1.0
Archived: 2026-10-16T10:35:05.813Z
SHA-256 of the archived text: 8e7de9f1581117bb8e131aa18a597660ee20f52f4c156d1eafe0ffe766bc18b7
Stable URL of this page: https://metraipolis.com/documento/dc_4abea87153fa

How to cite

DefoliationCAM: An Edge-AI System for Real-Time Insect Defoliation Severity Assessment in Grapevines — arXiv:2610.20879 — arXiv (en), https://arxiv.org/abs/2610.20879 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4abea87153fa (Metraipolis archive dc_4abea87153fa, sha256 8e7de9f1581117bb8e131aa18a597660ee20f52f4c156d1eafe0ffe766bc18b7)

BibTeX

@misc{dc4abea87153fa,
  title = {DefoliationCAM: An Edge-AI System for Real-Time Insect Defoliation Severity Assessment in Grapevines — arXiv:2610.20879},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.20879},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4abea87153fa; sha256 8e7de9f1581117bb8e131aa18a597660ee20f52f4c156d1eafe0ffe766bc18b7},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.20879. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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. 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. The 29,674 field-collected patches were labeled as background or one of four damage levels: no to negligible, mild, moderate, and severe. Merging moderate and severe classes reduced the class imbalance ratio from approximately 40:1 to 8.8:1. Synthetic augmentation increased macro-F1 from 0.768 to 0.838. The classifier outperformed ResNet-50 in macro-F1 with fewer parameters (1.08 M versus 23.52 M). 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. Quadratic discriminant analysis identified boundaries of 9% and 29% separating negligible damage, mild damage requiring monitoring, and action-level damage warranting on-site scouting and insecticide treatment consideration. The 29% boundary aligned with the 25%-30% action threshold range for Japanese beetle management in grapevine. Using TensorRT FP16 on an NVIDIA Jetson Orin Nano, the pipeline processed each 4K image in approximately 1.5 s, with a complete operating cycle of 57-60 s. A five-week deployment of two field units demonstrated the feasibility of autonomous solar-powered operation, LoRa communication, and remote defoliation monitoring. D-CAM supports frequent, objective canopy damage assessment for targeted scouting and timely pest-management decisions.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2610.20879 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T08:52:02.619Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).