Wheat yield level has a direct impact on national food security, because wheat is a main food crop (Wei, Zhang, Shi, Wang, Chen, Song, &Tao, 2015). Low temperature freezing injury is one of the main natural problems affecting wheat growth (Sofalian, Mohammadi, Aharizad, Moghaddam, &Shakiba, 2006), and it occurs in the seedling stage, which results in losses in the agricultural production (Skinner&Mackey, 2009; Kolesnichenko, Pobezhimova, Grabelnych, Tourchaninova, Korzun, &Koroleva, 2003). Slight freezing injury leads to different degrees of yield reduction, whereas severe freezing injury will lead to low yield and serious losses in agricultural production (Bjarko, 1988; Wang, Wang, Wang, Wang, &Yin, 2011). Therefore, the detection of freezing injury is necessary for agricultural breeding and guidance. Gao et al(2006)and Ye et al(2009) determined the effects of low temperature stress on crops by judging the changes of chemical elements in seedlings. However, these methods are time-consuming and laborious; they hinder promotion and application. Therefore, for food security, a rapid and non-destructive diagnostic method of wheat low temperature stress is needed to accelerate the breeding of excellent frost resistance wheat genotypes (Wu, Zhu, Cheng, Ma, &Wang, 2012; Cobb, Declerck, Greenberg, Clark, &Mccouch, 2013). Optical method has advantages in rapid nondestructive testing. Therefore, many researchers have tried various optical sensors to evaluate crop growth (Busemeyer, Lucas, Mentrup, Daniel, Möller, Kim, Erik, Alheit, &Katharina, 2013; Zhou, Mou, Zhou, Zhou, Heng, &Nguyen, 2021; Bodner, Alsalem, Nakhforoosh, Arnold, &Leitner, 2017). In terms of freezing damage detection, Li, Chen, Yang, and Zhang(2006) conducted a preliminary study on chlorophyll fluorescence characteristics of cotton seed seedlings under low temperature stress. Wu, Zhu, Cheng, Ma, and Wang(2012) studied the degree of freezing damage of wheat seedlings in multiple periods by using imaging spectrum and image integration technology, which can accurately reflect the parts of wheat seedlings with freezing damage. For the large-scale measurement of freezing injury, remote sensing can potentially measure freezing injury (Feng, Yang, Cao, &Ding, 2009). Remote sensing monitoring was used to assess the freezing injury of spring wheat, summer maize (Zhang, Chen, Su, & Zhou, 2001), and winter wheat (Zhang, Chen, Zheng, Zou, Chen, &Fu, 2006) in Ningxia. To systematically evaluate the severity of winter wheat freezing injury, Wang et al. (Wang, Guo, Wang, Huang, Xiao-He, &Dong, 2013) proposed a grey system model (GSM) to monitor the extent and distribution of winter wheat freezing injury. However, the studies used too-low spatial resolution of remote sensing data; thus, the monitoring effect is not satisfactory. Truss-type phenotypic equipment is widely used for the complex field environment; it has the advantages of high acquisition accuracy, good stability, high imaging quality, and all-day plant monitoring (Virlet, Sabermanesh, Sadeghitehran, &Hawkesford, 2017; White, Andrade-Sanchez, Gore, Bronson, Coffelt, &Conley, 2012).
The field mobile phenotype cabin is a large-scale phenotype platform that acts on the field environment to realize the functions of real-time climate simulation, full coverage of crop varieties, and remote real-time tracking simulation. The device is based on the RGB FREEZING INJURY SYSTEM, which is composed of a mobile warehouse in the field and an RGB camera. It is used to realize the automatic and efficient acquisition and identification of wheat canopy freezing injury images. In addition, the software of data acquisition control and phenotype extraction was specifically developed to facilitate shooting and operation. In this paper, the RGB images of different wheat genotypes obtained from the phenotypic warehouse in the field were used to analyze and calculate the wheat seedling stage vegetation coverage. Through image processing technology, the freezing injury part of winter wheat was analyzed and identified, and the freezing injury level was divided to provide reference information for wheat breeding and agricultural activities.