## 3 Results and Discussion
### Ablation Experiments  
To comprehensively evaluate the effectiveness of the constructed ECA-DenseNet model and deeply analyze the role of each module in the overall model performance, this study conducted ablation experiments [41] focusing on three key modules: BCA, ACMix, and ECA. The performance of the model was investigated in terms of metrics such as Acc, Kappa, MCC, Parameters, Size [42], and FLOPs [42]. The experiments adopted both forward and reverse ablation methods to quantitatively analyze the contribution of each module, with the results shown in Table 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Kappa (\%)** & **MCC (\%)** \\ \hline
**ECA-DenseNet** & **99.42** & **99.01** & **99.11** \\ ResNet50 & 97.72 & 96.88 & 97.18 \\ MobileNetV3 & 94.11 & 95.03 & 95.01 \\ InceptionV3 & 96.10 & 96.43 & 96.32 \\ GhostNet & 95.12 & 94.93 & 95.42 \\ GoogleNet & 89.72 & 89.88 & 89.88 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison of ECA-DenseNet and other deep learning models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Training Time (Seconds)** & **Parameters (KB)** \\ \hline
**ECA-DenseNet** & **310** & **28,774** \\ ResNet50 & 911 & 92,148 \\ MobileNetV3 & 256 & 16,381 \\ InceptionV3 & 921 & 92,968 \\ GhostNet & 108 & 7031 \\ GoogLeNet & 301 & 26,562 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison table of training time and parameter scale between ECA-DenseNet and other deep learning models.  
In terms of accuracy, when all three modules (BCA, ACMix, ECA) were enabled, the accuracy reached the highest level of 99.42%, surpassing all the other combinations. When only single modules or partial combinations were enabled, the accuracy was relatively lower. For example, the accuracy was 94.85% when no modules were enabled. Similar trends were observed for the consistency coefficient (Kappa) and Matthews Correlation Coefficient (MCC). With all the three modules enabled, Kappa reached 99.01% and MCC reached 99.11%, both achieving optimal values among all the combinations.  
Regarding the model resource consumption metrics, Parameters, Size, and FLOPs increased with the enabling of the modules. For instance, in terms of the parameter count, the value was 26.1 M when no modules were enabled, increasing to 28.1 M when all three modules were enabled. The model size increased from 5.5 M (no modules) to 6.5 M (all modules), and the number of floating-point operations (FLOPs) rose from 5.5 M to 6.5 M. This indicates that while the introduction of modules significantly improves the model performance, it inevitably leads to increased model complexity and resource requirements.  
The ablation experiments validate the rationality and the effectiveness of the ECA-DenseNet model, which significantly enhances the performance while slightly increasing the size and computational costs. By innovatively integrating lightweight modules and at  
Figure 8: Radar chart for multi-performance comparison of ECA-DenseNet and other deep learning models.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**BCA** & **ACmix** & **ECA** & **Accuracy (\%)** & **Kappa (\%)** & **MCC (\%)** & **Parameters (M)** & **Size (M)** & **FLOPs (M)** \\ \hline \(\surd\) & & & 94.85 & 94.83 & 94.92 & 26.1 & 3.35 & 5.5 \\ \(\surd\) & & & 96.82 & 96.62 & 96.36 & 26.3 & 3.52 & 5.6 \\  & \(\surd\) & & 97.12 & 96.88 & 96.76 & 27.6 & 3.71 & 6.2 \\  & \(\surd\) & & 96.15 & 95.89 & 95.98 & 26.4 & 3.47 & 5.7 \\ \(\surd\) & \(\surd\) & \(\surd\) & **99.42** & **99.01** & **99.11** & **28.1** & **3.92** & **6.5** \\  & \(\surd\) & & 98.64 & 98.72 & 98.34 & 27.9 & 3.86 & 6.4 \\ \(\surd\) & & \(\surd\) & & 98.52 & 98.09 & 98.66 & 26.6 & 3.52 & 5.8 \\ \(\surd\) & \(\surd\) & & 98.67 & 98.57 & 98.46 & 27.8 & 3.80 & 6.3 \\ \hline \end{tabular}
\end{table}
Table 8: Performance Parameter Comparison of BCA/ACmix/ECA Ablation Experiments.  
tention mechanisms into DenseNet, this model achieves efficient and accurate identification of tea tree seed varieties.