## 1 Introduction  
Out of the three key cereals, about 70% of wheat cultivation areas fall under arid and semi-arid agricultural zones [1]. Statistics show that droughts strike China 7.5 times on average every year, while the average afflicted crop area ranges from 20 to 30 million \(\mathrm{hm}^{2}\) and the average reduction in grain output amounts to 250-300 billion \(\mathrm{hm}^{2}\) each year, thus creating very serious challenges for grain production and security [2]. Severity, duration, timing, and location of the drought are all factors that can alter the effects of drought on wheat yield and quality. From the present research work, it is clear that the reduction in wheat yield was proportional to both the degree of drought stress and the specific growth stage during which the drought happened [3]. In particular, drought stress during jointing, heading, and grain-filling has a huge effect on wheat growth and output, reducing its quantity and quality [4]. This will require timely data on wheat drought monitoring, correct identification of drought stress, and timely irrigation for early warning and mitigation to ensure improved grain yield. Traditional approaches to assessing soil and crop water status encompass a range of techniques such as agricultural meteorological observations, direct soil moisture assessments, thermal infrared imaging, hyperspectral analysis, chlorophyll fluorescence detection, and manual evaluation. While each of these methods offers insights into the state of drought in crops, they all come with inherent delays or constraints that may affect their accuracy or timeliness [5]. In farmland irrigation areas, the information from agricultural meteorological drought monitoring is somewhat limited. Irrigation may alter the status of soil moisture but cannot alter the humidity and temperature in time, which is monitored by meteorological monitoring systems [6]. In contrast, general indirect monitoringis soil moisture monitoring, but because it is not very accurate and does not have a large scope of coverage, application is somewhat constrained [7]. Scientists utilize a suite of advanced imaging techniques, including thermal infrared, hyperspectral, and chlorophyll fluorescence, to directly assess water stress in crops. These methods are instrumental in evaluating the hydration status of the plant canopy and foliage [8]. For example, [PERSON] et al. used thermal infrared imaging to analyze maize drought resistance and explored rapid and efficient selection of winter wheat drought-resistant varieties [9]. [PERSON] et al. utilized high-resolution thermal infrared images to explore the relationship between canopy temperature and soil moisture [10]. Although thermal infrared imaging is capable of detecting signs of drought stress in crops, its ability to cover large areas is limited and it can be affected by various environmental factors as well as the specific types of crops [11]. Hyperspectral technology, which reflects crop stress states through spectral features [12], is widely applied in the surveillance of crop water scarcity; the spectrum that indicates drought sensitivity typically spans from 1200 nm to 2500 nm [13]. Chlorophyll fluorescence is a responsive indicator for detecting the initial signs of water scarcity in crops, yet it faces difficulties when it comes to tracking more extreme drought conditions. The existing technology for measuring chlorophyll fluorescence is predominantly limited to the examination of small-scale plants or those in their early growth phases. Overseeing extensive agricultural fields or the specific characteristics of crops can be quite a daunting task. Nonetheless, the evolution of computer vision and image processing has opened new avenues. Nowadays, deep learning algorithms that utilize two-dimensional digital photographs are extensively applied to distinguish and categorize various types of crop stress, whether caused by living organisms or non-living factors [14].