# Yielding Multi-Fold Training Strategy for Image Classification of Imbalanced Weeds
## 5 Experiments
### Analysis
Figure 24 shows examples of images that were incorrectly predicted by the models trained  
Figure 23: Number of images that were correctly classified using conventional training method but incorrectly classified using YMufT (CtYf), and vice versa (CfYt). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Method** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{8}{*}{PlantSeedlings} & YMufT, Mobilenet & 0.9637 & 0.9614 & 0.9587 & 0.9596 \\  & YMufT, Resnet & 0.9718 & 0.9713 & 0.9672 & 0.9689 \\  & YMufT, NASNet & 0.9682 & 0.9665 & 0.9643 & 0.9652 \\  & 3 models, weighted linear & 0.9704 & 0.9706 & 0.9659 & 0.9683 \\  & Combination (per-species) [23] & 0.9364 & 0.9300 & 0.9400 & 0.9300 \\  & AggroAVNET 12 species [24] & 0.9621 & 0.9525 & 0.9583 & 0.9542 \\  & ResNet-50 [25] & 0.9110 & - & - & - \\  & CFMNN [26] & 0.9523 & 0.9500 & 0.9500 & 0.9500 \\  & ResNet 50 [27] & 0.9350 & 0.9400 & 0.9400 & 0.9300 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: \(Cont.\)with the conventional method but correctly classified by those trained using YMufT. On the CNU Weeds dataset, YMufT helped the models focus on the leaf surface, while conventional training was more focused on high-level features. Specifically, for the species _Veronica persica_ [PERSON] and _Amarantthus powellii_ [PERSON], conventional training focused on the lower curve position, while YMufT concentrated on leaf veins and leaf blades. A similar phenomenon also occurred on _Ambrosia artemisifolia L., Ipomoea occinea L., and _Chenopodium album_ L. species. The only image from _Chenopodium ficifolium_ [PERSON] contains overlapping leaves, so the YMufT strategy guided the model to localized areas around leaf centroids, while the conventional training method focused on a lower region. Similar things happened on the large PlantVillage dataset, such as for images of Cherry_healthy, Tomato_Late_blight, Tomato_Bacterial_spot, Apple_Apple_scab, and Potato_Early_blight. For one image of Pepper_bell_Bacterial_spot, both methods focused on the leaf surface, but the YMufT strategy mainly concentrated on the petiole region. Generally, on large datasets, YMufT helped the model focus on the leaf surface to capture essential low-level features. In contrast, the conventional training method focused on high-level features such as leaf curves, which may not be significant in terms of species characteristics. Figure 24: _Cont._  
Figure 24: Grad-CAM visualization of models trained by the conventional method and YMufT strategy. The original images were misclassified using the conventional method but classified correctly using YMufT. Below each original image is the species name. In contrast, both the conventional training and YMufT strategies concentrated on the surfaces in images in the small PlantVillage dataset. Still, the heatmap color shown in Potato_healthy, Apple_Cedar_apple_rust, and Tomato_Leaf_Mold indicated that YMufT made the model pay more attention to the local area in the surface and reduced its attention to patterns in the background. Other images show that the two methods displayed different feature localization behavior. The conventional training method focused on the wide region in the middle of the leaf in Corn_Cercospora_leaf_spot Gray_leaf_spot, but the YMufT strategy looked at micro-characteristics towards the top. In Corn_Northern_Leaf_Blight, the small region near the middle was not sufficient to classify this image, so YMufT extended the region towards both sides to collect additional essential features. Consider Potato_Late_blight image: although both methods focused on the left half of the image, the conventional method mainly focused on the upper and lower curve, while YMufT considered the inner region to capture essential features. In the PlantSeedlings dataset, low-quality images, as well as the small shapes of parts of the weeds, meant that the model trained by the conventional method suffered from difficulty in localizing the target weeds.