# Use of CNN for Water Stress Identification in Rice Fields Using Thermal Imagery
## 1 Introduction  
Many studies have shown that water stress in rice reduces its yield and quality [1; 2; 3]. [PERSON] et al. [4] observed that drought reduced the average rice yield by 6.8% in China. In addition, [PERSON] et al. [5] evaluated the effects of three water levels (100%, 70%, and 40%) on three rice varieties, showing that the grain yield was reduced by 20.74-32.51% at the 70% water level and by 45.34-53.08% at the 40% water level. Furthermore, [PERSON] et al. [6] reported that under 75% and 50% water saturation, the panic number/rice plant was approximately one, while it was five under saturated conditions. [PERSON] et al. [7] also showed that reduced irrigation significantly decreased rice yield. These studies illustrate the importance of water to rice yield; however, unfortunately, many meteorologists forecast that global droughts will become more severe in future [8; 9; 10; 11], thereby causing food supply problems in countries where rice is the staple food. Taking other plants as an example, [PERSON] et al. [12] emphasized that chickpea yields were drastically reduced due to drought. In response to this predicament, many studies have proposed various solutions, such as the stricter management of irrigation [13]. In addition, botanists are attempting to cultivate new drought-tolerant rice varieties [14; 15; 16] to reduce water usage. However, these proposals require an automatic irrigation monitoring system; therefore, the purpose of this article was to develop water stress identification for rice fields. The proposed system is based on leaf temperature. Since plant leaves contain stomata, the opening and closing of the stomata are affected by water irrigation. When there issufficient water, the stomata often open; otherwise, the stomata are mostly closed. If the stomata are open, the leaf temperature is lower, and vice versa [17; 18]. A thermal imaging camera is a suitable choice for simple measurement of leaf temperature as it can record leaf temperature by taking pictures and does not require the manual measurement of each leaf. In addition, it is a non-contact measurement, so causes no damage to the leaf. Therefore, thermal imaging cameras are widely used to measure the temperature of various plants [19]. Recently, the CNN has become the most successful method for image recognition [20] and is commonly used to identify plant phototypes. For example, the VGG16 model has been used to identify chickpea seed varieties [21] and different varieties of grape leaves [22], while DenseNet has been used to identify PlantVillage (a well-known plant disease database) [23]. These applications show that CNNs are advantageous for plant phototype identification. Typically, a CNN extracts features by training convolution kernel weights and uses these features for recognition by training fully connected layers. In this article, a CNN was used to identify water stress in rice from thermal images, adopting three well-known CNN architectures as the network backbone [24; 25; 26]. In addition, [PERSON] et al. [27] proposed a plant leaf water content estimation system based on visible light cameras. In their method, six texture features were extracted in three color spaces and used to estimate the leaf water content. This usually requires more prior knowledge, while texture features require stricter shooting conditions; for example, the shooting distance must be sufficient to capture the texture. Since thermal images directly reflect the water content of the leaves, we do not need to consider prior knowledge or shooting conditions. Therefore, using thermal imaging is a relatively easy way to estimate the water content of plant leaves. Recently, due to the rapid development of CNNs, the technology has been applied in many studies to identify the problem of water stress in plants. [PERSON] et al. [28] used three common CNN architectures (AlexNet, GoogLeNet, and Inception v3) to identify stress and non-stress in RGB images of corn, oka, and soybean. In addition, [PERSON] et al.