## 3 Results and Discussion
### Ablation Study  
(1) Evaluation on different components:  
We conducted related ablation experiments on the ACCF dataset to validate the performance of the different modules proposed. Table 1 lists the results obtained on ACCF. The baseline model only achieved an average accuracy (MIoU) of 82.9%, due to the original backbone network's inability to effectively capture the redundant features and edge texture characteristics of winter wheat in the dataset. After using RRG, the performance of the segmenter improved by 2.5%, indicating that RRG can retain redundant feature maps while reducing memory usage, thus optimizing the overall model performance without sacrificing accuracy. With the addition of MSAS, it enables high-quality feature extraction based on the corresponding target semantic information. In Table 1, a check mark (\(\checkmark\)) indicates the usage of our designed module, while a cross mark (\(\times\)) indicates otherwise. Our MSAS module has established a robust feature representation capability, leading to a 0.8% performance improvement in our RRMSA-Net. The addition of RRG resulted in a 1.5% increase in model accuracy, demonstrating that proper lightweight model design and multi-level spatial feature aggregation can further enhance segmentation precision. Similar experimental results were obtained on the VOC dataset. Compared to networks with a single module, those with a combination of different modules achieved better performance. The integration of RRG and MSAS brought about optimization in model computational efficiency and improvement in feature quality, thereby achieving superior segmentation accuracy and classification results. Furthermore, the experiments also showed that there were no conflicts between the proposed modules. When employing the proposed approach, the model demonstrated an optimal performance of 91.3% MIoU. \begin{table}
\begin{tabular}{c c c} \hline \hline
**With RRG?** & **With MSAS?** & **MIoU** \\ \hline \(\times\) & \(\times\) & 82.9\% \\ \(\checkmark\) & \(\times\) & 85.4\% \\ \(\times\) & \(\checkmark\) & 86.2\% \\ \(\checkmark\) & \(\checkmark\) & 91.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Effects of each component of RRMSA-Net on ACCF dataset. Evaluating the effectiveness of the Rapid Redundant Feature-Generation (RRG) module:  
From Table 2, we observe the following results for RRG (\(\times 10^{-3}\)) at different depths, d = 1, d = 3, d = 5, and d = 7, with corresponding values in Figure 5 red, yellow, and green categories as 4.0, 3.4, 3.3, 3.2; 25.0, 24.5, 24.2, 23.7; and 12.0, 11.1, 11.0, 10.9, respectively. All the Mean-Squared Error (MSE) values are very small, indicating strong correlations between feature maps in deep neural networks, and that these redundant feature maps can be generated from a few intrinsic feature maps. Based on this correlation, excess feature maps can be efficiently derived from core feature maps. Although convolution operations were primarily used in our experiments, other low-cost linear operations could also be considered for constructing this module. However, convolution, as an efficient operation, is already well-supported by hardware devices and can implement a variety of common linear operations such as smoothing, blurring, and motion processing. Theoretically, we could adjust the size of each filter in the linear operations, but such irregular modules might reduce the computational efficiency of processing units (like CPUs and GPUs). Therefore, in our RRMSA-Net, using the RRG module is the optimal choice for achieving the best performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MSE (10\({}^{-3}\))** & **d = 1** & **d = 3** & **d = 5** & **d = 7** \\ \hline Blue pair & 5.0 & 4.2 & 4.3 & 4.5 \\ Yellow pair & 35.0 & 36.5 & 34.2 & 33.7 \\ Green pair & 22.0 & 21.1 & 21.0 & 20.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between MSE error and different kernel sizes.