# Use of CNN for Water Stress Identification in Rice Fields Using Thermal Imagery
## 4 Discussion  
The softmax feature was also used for classification to explore whether the degree information features considered in this study are useful. The softmax feature is the model output in the second and third columns of Table 3. Using these two results, we use the same MLP as in Section 2.5 and the same setting for the classification task. The accuracy, shown in Table 5 and compared with the results of the proposed model in the last column in Table 3, is less accurate under the three CNN architectures. We also conducted an experiment to illustrate the necessity of MLP. By directly using the two scores of TLSef and TLSlf for water stress classification, we combined these two scores in a simple statistical way, using the mean, maximum, and minimum of the two scores. For these three scores, the following rules were used to identify the degree of water stress: if the score was within 1-0.667 we considered it well-watered; within 0.667-0.333 we considered it mildly stressed; and within 0.333-0 we considered it slightly stressed. The results are shown in Table 6. We reviewed the MLP performance in the last column of Table 3, which outperformed the results in Table 6, demonstrating that the MLP can effectively fuse the two scores in this application. Furthermore, it mostly outperformed the CNN with the softmax output, and the combination also outperformed the softmax feature-based in Table 5. This shows that our method retains more degree information. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**CNN Architecture** & \begin{tabular}{c} **Mean of TLSef** \\ **and TLSlf** \\ \end{tabular} & \
\begin{tabular}{c} **Maximum of TLSef** \\ **and TLSlf** \\ \end{tabular} &
\begin{tabular}{c} **Minimum of TLSef** \\ **and TLSlf** \\ \end{tabular} \\ \hline VGG16 & 80.48\% & 78.05\% & 92.68\% \\ ResNet34 & 65.85\% & 82.93\% & 43.90\% \\ DenseNet121 & 68.29\% & 68.54\% & 60.98\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Result of accuracy of three statistical values. Figure 7: The confusion matrix of the three CNN architectures. In order to clearly compare Tables 5 and 6 and the results of the method in this study (mean of TLS\({}_{\text{ef}}\) and TLS\({}_{\text{if}}\)), we drew them in Figure 8. Through this figure, it can be clearly seen that the mean of TLS\({}_{\text{ef}}\) and TLS\({}_{\text{if}}\) is superior to the others under the three CNN architectures. Next, we explored several single cases to better understand the advantages and disadvantages of the proposed method and provide directions for future work. First, let us discuss a few cases where identification was correct. The TLS\({}_{\text{ef}}\) and TLS\({}_{\text{if}}\) complemented each other's deficiencies through the use of two scores. Incidentally, although we only selected the correct cases due to the addition of TLS\({}_{\text{ef}}\) in the Table 7, there are also many clearly classified cases. For the convenience of comparison, we reraised the corresponding scores again, in ground truth, where well-watered was 1.0 point, slight stress was 0.5 points, and mild stress was 0.0 points. From Table 7, we can see the cases 1, 5, 6, and 9 all have unsatisfactory TLS\({}_{\text{if}}\), and all rely on the TLS\({}_{\text{ef}}\) score being closer to well-watered (1.0 point) to identify the case to correct. In cases 2, 3, 7, 8, 10, 11, and also cases 4 and 12, the ground truths were slightly stressed and mildly stressed, respectively, also to be corrected by TLS\({}_{\text{ef}}\). Then we listed several cases of wrong identification (Table 8) and found that when a wrong prediction occurred, their two scores were far from the correct result. This result indicates that to make the system more accurate, we need to start with two scoring branches.