Hybrid Edge AI and Augmented Reality System for Offline Herb Identification in Low-Connectivity Supply Chains

IEEE Annual Computing and Communication Workshop and Conference (CCWC) 2026

View at publisher
Authors: William Aparecido Celestino Lopes, Samira Nascimento Antunes, Sérgio Miele Ruggero, Marcelo Tsuguio Okano
DOI: 10.1109/ccwc67433.2026.11393833
Cited by: 0
References: 7

Abstract

This paper presents a comparative case study of two computer-vision artifacts designed for accurate herb identification in low-connectivity supply-chain environments. The first system employs a YOLOv8 deep-learning model optimized for Edge AI deployment, achieving${\geqslant} \mathbf{0. 7 0}$precision and$

Fields of Study & Subjects

Smart Agriculture and AI Advanced Neural Network Applications Industrial Vision Systems and Defect Detection

Publication details

Journal / source
IEEE Annual Computing and Communication Workshop and Conference (CCWC)
Pages
627–632
Publication date
5 January 2026
Language
EN