Unverified paper record
EV2M-YOLOv8: enhancing AI computer vision with EfficientNetV2 and multi-head self-attention for low-complexity agricultural crop, chilli maturity detection
Signal, Image and Video Processing · 9 Sept 2025 · 10.1007/s11760-025-04637-z
Abstract
Abstract has not been obtained from indexed metadata or an accessible article page.
Plant phenotyping relevance
唐辛子の成熟度という植物状態を画像認識で推定するAI手法の開発が題名の中心であり、植物フェノタイピング手法に該当する。
titleEV2M-YOLOv8: enhancing AI computer vision with EfficientNetV2 and multi-head self-attention for low-complexity agricultural crop, chilli maturity detection
Code and data availability
The paper trains EV2M-YOLOv8 on a chilli maturity image dataset (1377 original / 2480 augmented images), but no public dataset, code, or model deposit is provided. The Data availability statement explicitly says no datasets were generated or analysed, and the only GitHub links are to third-party tools (Ultralytics YOLO
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