# Yielding Multi-Fold Training Strategy for Image Classification of Imbalanced Weeds
## 5 Experiments
### Analysis  
Generally, on large datasets (CNU Weeds and large PlantVillage), training a model using the YMufT strategy rather than the conventional training method made the validation slower to converge. Still, the overall F1 score and the score on minor species improved, while the F1 score was maintained on major species. Of the three models, NASNet achieved the best performance on the CNU Weeds dataset, while Mobilenet was the optimal solution on the large PlantVillage dataset. On small datasets (small PlantVillage and PlantSeedlings), the models trained using the YMufT strategy were faster to converge on validation than those trained using the conventional training method. Furthermore, using the YMufT strategy, the overall performance of the models and the performance on minor species were significantly improved in comparison to the performance of the models trained using the conventional training method. Mobilenet and Resnet were the optimal models on the small PlantVillage dataset, and Resnet achieved the highest performance on the PlantSeedlings dataset. Table 5 compares the YMufT strategy to other methods. On the CNU Weeds dataset, the NASNet model trained using the YMufT strategy showed slightly lower performance than the other 2 DNN models. On the large PlantVillage dataset, Mobilenet trained using YMufT was the optimal solution. On the two small datasets, DNN models trained using YMufT were superior to other methods. The optimal models on the small PlantVillage dataset were Mobilenet and Resnet. Resnet was also the optimal model on the PlantSeedlings dataset. Figure 21: Average overall performance of 5-fold cross-validation on the PlantSeedlings dataset. Figure 20: Performance of models trained on the PlantSeedlings dataset, using conventional training (upper row) and YMufT strategies (lower rows). Figure 22: Precision, recall, and F1 score on each species in the PlantSeedlings dataset. The blue bars indicate the ratio of the number of samples of a species to the maximum number of samples in all species. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Method** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{4}{*}{CNU} & YMuTf, Mobilenet & 0.9673 & 0.9602 & 0.9583 & 0.9591 \\  & YMuTf, Resnet & 0.9795 & 0.9720 & 0.9681 & 0.9698 \\  & YMuTf, NASNet & 0.9802 & 0.9721 & 0.9698 & 0.9708 \\  & 2 models, product rule [23] & 0.9844 & 0.9725 & 0.9768 & 0.9746 \\ \hline \multirow{4}{*}{Large PlantVillage} & YMuTf, Mobilenet & 0.9942 & 0.9930 & 0.9926 & 0.9928 \\  & YMuTf, Resnet & 0.9937 & 0.9921 & 0.9918 & 0.9919 \\  & YMuTf, NASNet & 0.9927 & 0.9910 & 0.9918 & 0.9914 \\  & Convention, Mobilenet & 0.9922 & 0.9898 & 0.9902 & 0.9900 \\  & Convention, Resnet & 0.9938 & 0.9925 & 0.9918 & 0.9921 \\  & Convention, NASNet & 0.9937 & 0.9923 & 0.9920 & 0.9921 \\ \hline \multirow{4}{*}{Small PlantVillage} & YMuTf, Mobilenet & 0.9981 & 0.9970 & 0.9971 & 0.9970 \\  & YMuTf, Resnet & 0.9979 & 0.9967 & 0.9973 & 0.9970 \\  & YMuTf, NASNet & 0.9965 & 0.9944 & 0.9955 & 0.9949 \\  & SE-MobileNet [28] & 0.9978 & - & - & - \\  & Resnet50 [29] & 0.9959 & - & - & - \\  & DenseNet [29] & 0.9975 & - & - & - \\  & ReLU [30] & 0.9960 & - & - & - \\  & GoogleNet, transfer learning [31] & 0.9935 & 0.9935 & 0.9935 & 0.9934 \\  & ResNet34, deep [32] & 0.9967 & - & - & - \\  & Inception\_v3 [32] & 0.9976 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison between the YMuTf strategy and other methods. Figure 23 compares the number of images that were correctly classified using the conventional training method but incorrectly classified using YMufT (CtYf) and vice versa (CfYt). In all cases, use of YMufT effectively enhanced the models' ability to classify images in both minor and major species. To further explore the effectiveness of YMufT, we applied Grad-CAM [41] to visualize the behavior of the models trained by the conventional method and YMufT strategy.