# Use of CNN for Water Stress Identification in Rice Fields Using Thermal Imagery
## 1 Introduction
[29] built a model for water stress identification in corn using a simple six-layer convolutional network model to extract RGB image features to evaluate water stress with an SVM and an extra trees regressor. Both these methods are based on RGB images to build models; however, water stress is usually identified in such images by observing shrinkage on the surface of the plant leaf. For rice, such water stress is already very serious and greatly affects the production quality, so it is more appropriate to use thermal images to identify rice water stress. Several previous studies have used CNN to identify plant drought problems with thermal images. First, [PERSON] et al. [30] classified the irrigation of pomegranate trees as high (100% and 75%) and low (35% and 50%), then used a shallow (three-layer) CNN to identify the classes. [PERSON] et al. [31] built a water stress identification system for winter wheat. They classified irrigation conditions as stressed or non-stressed, then trained CNN models based on RGB and thermal images. They tested five state-of-the-art CNN architectures (AlexNet, GoogLeNet, Inception v3, MobileNetV2, and ResNet-50) and five well-known classifiers (logistic, KNN, ANN, SVM, and LSTM), showing that the model using thermal images performed better than using RGB images as input. [PERSON] et al. [32] identified the water stress of sugarcane using transfer learning to train thermal images on an Inception-ResNet-v2 architecture. In order to clarify the above literature, we list several important factors [27; 28; 29; 30; 31; 32] in Table 1, as well as some remarks. For a more detailed study on the drought tolerance ability during the development of drought-tolerant varieties, the degree of rice water stress must be classified precisely. In addition, rice depends heavily on water; therefore, if water stress is very high, rice will not survive. Consequently, in our work, 100% and 90% irrigation are regarded as well-watered and slightly stressed for rice, while 80% normal irrigation is considered mildly stressed. In addition, leaf temperature is affected by the amount of irrigation to a different degree. The above studies [30; 31; 32] either refer to two classes or their target plants do not need such fine classification, so they usually ignored degree information. To pay more attention to degree information and to make the identification of water stress more accurate, a framework was designed to consider the degree information. In addition, leaf temperature is not an absolute value and is affected by the surrounding environment, which is also reflected in CWSI calculations used in many studies on plant water stress. CWSI [33; 34] takes the leaf temperature minus the air temperature as normalization, while this study refers to the background as the temperature reference. If acceptable accuracy can be achieved using the background as a reference, then it is not necessary to record the current temperature. Referring to Table 1, we have sorted out the main contributions of this study:  
1. We chose thermal images instead of RGB images as the input data and explain its advantages in our subsequent discussion section. 2. A CNN was selected as a feature extractor and classifier, which does not require prior knowledge of hand-crafted features. In addition, we used a scoring method to retain degree information. 3. We used two background processing methods: early fusion and late fusion which complement each other. 4. For other plants or species, a framework is provided for the assessment of the water content of other plant leaves. This study comprises five sections: Section 2 describes the data collection and their classification methods as well as the methods used in this study; in Section 3, we designed several experiments for comparison to illustrate the effect of the method in this study; Section 4 discusses the proposed method in-depth; and the conclusion and future work are provided in Section 5.