# Use of CNN for Water Stress Identification in Rice Fields Using Thermal Imagery
## 3 Experimental Results  
The results of the three well-known CNN architectures, VGG16, ResNet34, and DenseNet121, are in the second column. The last layer of the early fusion branch model in Section 2.3 was changed into three output neurons and the softmax function was used for classification. This approach is similar to [30; 31; 32], with some minor differences such as the different CNN architectures. The third column is the result of changing the last layer to three output neurons and using the softmax function in the model in Section 2.4. The fourth column is the method proposed in this study, as shown in Figure 2. Under the three CNN architectures, the method of separating the background and the foreground (the third column of Table 3) is significantly better than in the second column of Table 3, which shows that the late fusion of the background and the foreground significantly improves identification. The results in the fourth column are much higher than those in the previous two columns and are a significant improvement on the results of the late fusion method, which shows that the early fusion branch compensates for the deficiencies of the late fusion method.  
To further examine these results, we calculated precision, recall, and F1-score. The calculation of these three evaluation metrics are shown in Equations (2)-(4):  
\[Precision=\frac{TP}{TP+FP}, \tag{2}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{3}\]  
\[F1-score=2\frac{Precision\times Recall}{Precision+Recall}. \tag{4}\]  
_TP_ is true positive, which means that the rice field is facing water stress and the model correctly predicted water stress. _FP_ is false positive, which means that the rice field is well-watered but the model wrongly identified water stress. _FN_ is false negative, which means that the rice field is facing water stress but the model wrongly predicted that it is well-watered. _TN_ is true negative, which means that the rice field is well-watered and the model correctly predicted that it is well-watered.  
The precision in Table 4 is excellent, indicating that the proposed model performs well for the misjudgment of water stress. The recall is also relatively good, indicating that the system has a good ability to detect water stress.  
The confusion matrix of the three CNN architectures was drawn to describe detailed identification results, as shown in Figure 7. Most of the misjudgments were between slight stress and mild stress for ResNet34 and DenseNet121, thereby requiring further enhancements.  
\begin{table}
\begin{tabular}{c c c c} \hline
**CNN Architecture** & **Precision** & **Recall** & **F1-Score** \\ \hline VGG16 & 1.0000 & 1.0000 & 1.0000 \\ ResNet34 & 0.9200 & 1.0000 & 0.9019 \\ DenseNet121 & 1.0000 & 0.9231 & 0.8695 \\ \hline \end{tabular}
\end{table}
Table 4: Precision, recall and F1-score under the three CNN architectures.  
In addition, VGG16 performed better than the other two architectures. Since VGG16 does not have a cross-layer connection structure in the network, it makes the temperature information less mixed when it is in the convolutional layer, also indicating that the temperature data is more suitable for late fusion processing.