GCF-DeepLabv3+: An Improved Segmentation Network for Maize Straw Plot Classification

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.