# Evaluation Model of Rice Seeding Production Line Seeding Quality Based on Deep Learning
## 3 Results
### Comparison of the Results of Different Models  
In a bid to further validate the improved ResNet50 model's capabilities, it was contrasted with leading models in image classification, specifically AlexNet, DenseNet, and VGG16, through comparative experiments. These assessments, carried out under identical dataset and operational conditions, focused on evaluating training accuracy, validation accuracy, test accuracy, and average detection time. The experimental comparison results for these various classification models are presented in Table 4.  
From the data presented in Table 4, it is clear that the improved ResNet50 model exhibits an increased accuracy in the training set by 2.29%, 2.14%, 3.88%, and 0.06% compared to ResNet50, AlexNet, DenseNet, and VGG16, respectively. It also shows a notable improvement in validation set accuracy, with a 3.46%, 1.73%, and 2.85% increase over ResNet50, AlexNet, and DenseNet, respectively, and maintaining parity with VGG16. For test set accuracy, it records gains of 3.38%, 4.55%, 2.07%, and 2.62%, respectively. Improvements in average precision were noted at 5.65%, 5.91%, 3.56%, and 4.16%, respectively. Similarly, there were enhancements in the average recall by 5.43%, 4.55%, 2.06%, and 2.63%, respectively. Additionally, the F1 score displayed increases of 5.54%, 5.23%, 2.81%, and 3.39%, respectively. Despite the close training and validation set accuracies of the VGG16 model to the improved model, its lower test set accuracy indicates the likelihood of overfitting during training. In terms of computational efficiency, the improved ResNet50 model has a shorter detection time compared to AlexNet and DenseNet but is slightly slower than VGG16. Nevertheless, with all models performing under the RTX 4000 chip, the maximum disparity in average detection times is less than 20 ms, ensuring compliance with the speed requirements of production environments. These results affirm that the performance of the improved ResNet50 model is more advanced compared to other conventional classification models in the domain of the rice sowing dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{
\begin{tabular}{c} **Train** \\ **Accuracy/\%** \\ \end{tabular} } & **Val** & **Test** & **Average** & **Average** & **Average** \\  & & **Accuracy/\%** & **Accuracy/\%** & **Precision/\%** & **Recall/\%** & **F1\_Score/\%** & **Detection** \\ \hline ResNet50 & 97.23\% & 95.41\% & 92.44\% & 91.71\% & 90.40\% & 91.05\% & 7.5412 \\ AlexNet & 97.38\% & 97.14\% & 91.27\% & 91.45\% & 91.28\% & 91.36\% & 18.2530 \\ DenseNet & 95.64\% & 96.02\% & 93.75\% & 93.80\% & 93.77\% & 93.78\% & 28.2884 \\ VGG16 & 99.46\% & 98.87\% & 93.20\% & 93.20\% & 93.20\% & 93.20\% & 3.5903 \\ Improved & 99.52\% & 98.87\% & 95.81\% & 97.36\% & 95.83\% & 96.59\% & 14.3897 \\ ResNet50 & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \end{tabular}
\end{table}
Table 4: Recognition efficiency of different models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Epoch** & **Rate Value** & **Train Accuracy/\%** & **Val Accuracy/\%** \\ \hline Adagrad-ResNet50 & 50 & 0.01 & 93.57\% & 92.01\% \\ RMSprop-ResNet50 & 50 & 0.01 & 98.31\% & 97.42\% \\ SGD-ResNet50 & 50 & 0.01 & 97.44\% & 96.72\% \\ Adam-ResNet50 & 50 & 0.01 & 99.52\% & 98.87\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The impact of different optimizers on ResNet50.