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A feature-preserving and imbalance-aware network for strawberry maturity detection

delete2026-08-11
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PRE
AI
P
Pan Zhang
S
Sitao Liu
C
Conghui Zhao
H
Hongye Zhu
Y
Yingyi Chen *
D
Daoliang Li *
DOI：10.1016/j.compag.2026.112291delete
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Abstract

Abstract

En 中文
• AD-YOLO26 integrates ADown and DySample to enhance feature representation. • The improved structure boosts strawberry maturity detection accuracy. • Class-weighted BCE alleviates imbalance and strengthens minority learning. • Multi-fruit validation shows strong generalization and 327.63 FPS speed.
Keywords:
Strawberry
AD-YOLO26
Feature preservation
Class imbalance
Maturity detection

Journal

Computers and Electronics in Agriculture cover
Computers and Electronics in Agriculture
IF:
8.9
Papers:
9.9K
Citations:
4.8W

Organization

C
china agricultural university
Scholars:
4.9W
Papers: 2.9W
Citations: 43
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