文献类型： 外文期刊
作者： Liu, Yuanyuan 1 ; Zhang, Jiaxin 1 ; Wang, Yueyong 3 ; Luo, Yang 4 ; Sui, Pengxiang 4 ; Ren, Ying 4 ; Liu, Xiaodan 4 ; Wang, Jun 3 ;
作者机构： 1.Jilin Agr Univ, Coll Informat & Technol, Changchun 130118, Peoples R China
2.Jilin Agr Univ, Smart Agr Res Inst, Changchun 130118, Peoples R China
3.Jilin Agr Univ, Coll Engn & Technol, Changchun 130118, Peoples R China
4.Jilin Acad Agr Sci, Changchun 130033, Peoples R China
关键词： conservation tillage; DeepLabv3+; remote sensing; straw coverage; semantic segmentation
期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）
ISSN：
年卷期： 2025 年 15 卷 5 期
页码：
收录情况： SCI
摘要： To meet the need of rapid identification of straw coverage types in conservation tillage fields, we investigated the use of unmanned aerial vehicle (UAV) low-altitude remote sensing images for accurate detection. UAVs were used to capture images of conservation tillage farmlands. An improved GCF-DeepLabv3+ model was utilized for detecting straw coverage types. The model incorporates StarNet as its backbone, reducing parameter count and computational complexity. Furthermore, it integrates a Multi-Kernel Convolution Feedforward Network with Fast Fourier Transform Convolutional Block Attention Module (MKC-FFN-FTCM) and a Gated Conv-Former Block (Gated-CFB) to improve the segmentation of fine plot details. Experimental results demonstrate that GCF-DeepLabv3+ outperforms other methods in segmentation accuracy, computational efficiency, and model robustness. The model achieves a parameter count of 3.19M and its FLOPs (Floating Point Operations) is 41.19G, with a mean Intersection over Union (MIoU) of 93.97%. These findings indicate that the proposed GCF-DeepLabv3+-based rapid detection method offers robust support for straw return detection.
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