## 4 Experimental Results and Analyses
### Comparison with Other Methods
#### 4.2.3 Experiments on the URCAL Dataset  
On the URCAL dataset, we compared MS-WFFNet with mainstream plant counting methods, and the detailed results are shown in Table 5. As can be seen from the data in the table, P2 PNet-EFF [34] performs best in the counting scenario dominated by small-scale objects. With the EFF module converting the channel dimension into a feature sequence and inputting the Transformer encoder layer to enhance the model's attention to the overall morphology of the plant, P2 PNet-EFF achieved the lowest MAE and RMSE, which are 9.9 and 13.5, respectively. Our method retains the shallow small-scale object information by densely connecting the feature maps output by multiple position layers before and after \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Venue** & **MAE** & **RMSE** \\ \hline MCNN [35] & CVPR 2016 & 110.2 & 173.2 \\ Switch-CNN [50] & CVPR 2017 & 90.4 & 135.0 \\ CSRNet [48] & CVPR 2018 & 68.2 & 115.0 \\ SANet [43] & ECCV 2018 & 67.0 & 104.5 \\ IA-DCCN [51] & AVSS 2019 & 66.9 & 108.4 \\ SDANet [52] & AAU 2020 & 63.6 & 101.8 \\ Crowd-SDNet [53] & IEEE IT 2021 & 65.1 & 104.4 \\ GANNet [54] & CVPR 2022 & 59.2 & 95.4 \\ SRFNet [55] & IEEE T-ITS 2023 & 60.8 & 103.0 \\ SCPNet [49] & IJJ 2023 & **57.3** & 102.1 \\ ASANet [46] & JEI 2024 & 62.8 & 104.1 \\ RAQNet [56] & IEEE T-CE 2024 & 59.0 & 101.2 \\ Ours & This Paper & **57.3** & **85.2** \\ \hline \hline \end{tabular} Note: the best results are highlighted in **bold**, and the second-best results are double-underlined.  
\end{table} 
Table 4: Experimental results on the ShanghaiTech PartA dataset. the EEMA-VGG16 sub-network, thereby achieving lower MAE and RMSE, ranking third and second among all methods, respectively. As shown in Figure 9, we selected four representative images from the URCAL dataset to compare the prediction results of MS-WFFNet with several classic counting algorithms. The first two columns display the original images and their GT density maps, while the last five columns show the predictions from MS-WFFNet, RPNet, ASANet, FIDTM, and CSRNet. Overall, the density map predicted by MS-WFFNet, as well as the point maps predicted by RPNet and FIDTM, demonstrate high quality. In contrast, the density maps generated by ASANet and CSRNet exhibit a noticeable reduction in quality, directly impacting their counting accuracy. From a local perspective, while ASANet attempts to capture multi-scale information by integrating features from different layers, it struggles to effectively differentiate key features from irrelevant ones during the fusion process. This limitation compromises the quality of the generated density maps. Furthermore, repeated traditional upsampling operations exacerbate the issue by causing the inevitable loss of small-scale information, further affecting the model's performance. Figure 8: Visualization results of the various methods on the ShanghaiTech PartA dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Venue** & **MAE** & **RMSE** \\ \hline MCNN [35] & CVPR 2016 & 19.9 & 25.7 \\ CSRNet [48] & CVPR 2018 & 12.3 & 17.8 \\ SANet [43] & ECCV 2018 & 18.2 & 23.5 \\ TasselNet V2+ [57] & Front Plant Sci 2020 & 16.9 & 21.3 \\ P2 PNet [58] & ICCV 2021 & 15.2 & 18.8 \\ FIDTM [44] & TMM 2022 & 15.3 & 18.0 \\ RPNet [47] & Crop J 2023 & 10.3 & 14.0 \\ ASANet [46] & JEI 2024 & 14.9 & 19.5 \\ P2 PNet-EFF [36] & Agronomy 2024 & **9.9** & **13.5** \\ Ours & This Paper & 11.2 & 13.9 \\ \hline \hline \end{tabular} Note: the best results are highlighted in **bold**, and the second-best results are double-underlined.  
\end{table} 
Table 5: Experimental results on the URCAL dataset. RPNet achieves more accurate counting by incorporating a plant attention module and employing a joint loss function with multiple loss terms for multi-supervised training. However, as shown in the second row of prediction results, RPNet struggles with false detections when encountering similar background features, such as grassroots.