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UssNet: a spatial self-awareness algorithm for wheat lodging area detection

delete2025-08-21
delete0
PRE
AI
张
张珺 (Jun Zhang)
Q
Qiang Wu
F
Fenghui Duan
M
Mingzheng Feng
C
Cuiping Liu
戴
戴荔 (Li Dai)
X
Xiaochun Wang
S
Shuping Xiong
H
Hao Yang
G
Guijun Yang
S
Shenglong Chang *
X
Xinming Ma *
J
Jinpeng Cheng *
DOI：10.1016/j.eswa.2025.129433delete
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Abstract

Abstract

En 中文
• UssNet integrates State Space Models with UNet architecture for enhanced wheat lodging segmentation. • Achieves superior performance with significantly fewer parameters than competing CNN and Transformer methods. • Enables effective detection of small-scale lodging areas through improved boundary recognition capabilities. • Demonstrates robust cross-regional generalization across different flight altitudes and wheat varieties.
Keywords:
UssNet
State Space Models
UNet
wheat lodging segmentation
cross-regional generalization

Journal

Expert Systems with Applications cover
Expert Systems with Applications
IF:
7.5
Papers:
2.9W
Citations:
10.2W

Organization

B
Beijing Academy of Agriculture and Forestry Sciences
Scholars:
1.1K
Papers: 348
Citations: 4
H
Henan Agricultural University
Scholars:
1.4W
Papers: 6.1K
Citations: 9.3K
H
henan normal university
Scholars:
1.1W
Papers: 6.2K
Citations: 6
R
Research Institute of China
Scholars:
29
Papers: 22
Citations: 1
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