# Use of CNN for Water Stress Identification in Rice Fields Using Thermal Imagery
## 5 Conclusions  
This study proposed an identification system of water stress in rice fields. Leaf temperature is an important reference index for identifying water stress in rice and can be obtained relatively simply and conveniently using a handheld thermal imaging camera. The proposed automatic rice water stress identification system was developed for thermal images based on a CNN and achieved an accuracy of 85% to 100%, thus meeting the needs of practical applications. In the past, crop temperature-based water identification systems usually ignored degree information which can provide useful features in thermal images. We proposed a framework to incorporate degree information into the system, demonstrating through experiments that our method utilizes this information effectively. Since leaf temperature is relative, background temperature was used as a reference temperature, with two background processing methods used to obtain two temperature degree scores which complemented each other to achieve better results.  
In the future, the proposed system will be improved with better leaf temperature degree score extraction. Furthermore, since a MLP was used in the final score combination,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**CNN Architecture** & **VGG16** & **ResNet34** & **DenseNet121** \\ \hline Accuracy & 48.78\% & 56.10\% & 46.34\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Accuracy using RGB images.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**CNN Architecture** & **TC** & **TS** & **Ground Truth** & **Misjudgment** \\ \hline ResNet34 & 0.00104090 & 0.14355020 & Slight stress & Mild stress \\ ResNet34 & 0.54493946 & 0.48646522 & Mild stress & Slight stress \\ DenseNet121 & 0.99440467 & 0.99999380 & Slight stress & Well-watened \\ DenseNet121 & 0.78365338 & 0.99712139 & Mild stress & Slight stress \\ DenseNet121 & 0.67452341 & 0.9999666 & Mild stress & Slight stress \\ \hline \hline \end{tabular}
\end{table}
Table 8: Error case samples of \(\text{TLS}_{\text{ef}}\) and \(\text{TLS}_{\text{lf}}\) for the three CNN architectures.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**CNN Architecture** & **Tanh** & **Sigmoid** \\ \hline VGG16 & 100.00\% & 100.00\% \\ ResNet34 & 87.80\% & 80.49\% \\ DenseNet121 & 82.92\% & 85.37\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Accuracy of replacing MLP activation functions.  
other simpler methods can be considered to improve the computing speed. After further improvement, we believe that this framework may also be applied to other crops. Finally, for future field applications, we suggest replacing the handheld thermal imager with an external thermal imager connected to a mobile phone, thereby allowing for upload to the GPU host for real-time identification. This configuration is low-cost and easy to operate, making it accessible to farmers, scientists, and students.  
Methodology, Software and Writing--original draft, M.-W.L.; Supervision and Writing--review and editing, Y.-K.C. and S.-S.-S.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology, Taiwan, under grant number MOST 110-2321-B-005-005.  
Not applicable.  
Data Availability Statement: Data supporting this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.