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YOLO-MEST:A re-parameterized multi-scale fusion model with enhanced detection head for high-accuracy tea bud detection

Chuanyang Yu Yi Xue Liuyang Zhang Xue An Ce Liu Liqing Chen

Information Processing in Agriculture2026，Vol.13Issue(2)：P.163-175,13.

Information Processing in Agriculture2026，Vol.13Issue(2)：P.163-175,13.DOI:10.1016/j.inpa.2025.10.001

YOLO-MEST:A re-parameterized multi-scale fusion model with enhanced detection head for high-accuracy tea bud detection

Chuanyang Yu ¹Yi Xue ²Liuyang Zhang ²Xue An ²Ce Liu ²Liqing Chen²

作者信息

1. College of Engineering,Anhui Agricultural University,Hefei 230036,China Institute of Machinery and Electrical Engineering,Anhui Jianzhu University,Hefei 230601,China
2. College of Engineering,Anhui Agricultural University,Hefei 230036,China
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摘要

关键词

Computer vision/Tea bud/YOLO framework/Loss function/Picking localization

分类

农业科技

引用本文复制引用

Chuanyang Yu,Yi Xue,Liuyang Zhang,Xue An,Ce Liu,Liqing Chen..YOLO-MEST:A re-parameterized multi-scale fusion model with enhanced detection head for high-accuracy tea bud detection[J].Information Processing in Agriculture,2026,13(2):P.163-175,13.

基金项目

financially supported by State Key Laboratory of Tea Biology and Resource Utilization(Grant No.SKLTOF20230123,Project Holder:Ce Liu). （Grant No.SKLTOF20230123,Project Holder:Ce Liu）

Information Processing in Agriculture

ISSN：2097-0153

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