## 1 Introduction  
In Northeast China farmers burn agricultural residue (straw) to eliminate pests. However, straw burning sharply increases the ground temperature, which can directly destroy the living environment of beneficial microorganisms in the soil. It can indirectly reduce the absorption of soil nutrients by crops, as well as the yield and quality of the produce. Controlling the burning of straw is important for ensuring air quality and for sustainable agriculture [1]. The Beijing News reported that since 12 April 2020, heavy air pollution has affected northeast China, and the air quality index of many places has reached 500 [2]. Experts have found that the large-scale, high-intensity open-field burning of straw has been the main cause of heavy air pollution in Northeast China since 12 April 2020. In the rural areas of northeast China, due to the large-scale burning of straw in spring and autumn [3], the levels of inhalable particulate matter PM10 (aerodynamic diameter between 2.5-10 \(\upmu\)m) and fine particulate matter PM2.5 (aerodynamic diameter smaller than 2.5 \(\upmu\)m) can increase between 0.5 to 4 times, respectively [4]. In addition, straw burning also produces a large amount of pollution, such as carbon monoxide, which seriously reduces the quality of the atmospheric environment [5, 6]. To enforce the Environmental Protection Law and the Law of the Prevention and Control of Atmospheric Pollution, the local government has set up no-burning areas and restricted-burning areas. It is prohibited to burn straw from crops in the open air in these areas, which include the land surrounding urban areas, both sides of a highway, and around airports. It is essential to enforce environmental laws, restrict straw burning, and improve the understanding of the impact of straw burning on air quality. Straw burning smoke detection provides important basic data for straw burning control. Previous detection methods have mainly relied on environmental monitoring sites distributed in the area. The environmental monitoring sites can detect straw burning through smoke sensors, and it is important to monitor and measure the indicators reflecting environmental quality to determine the level of environmental pollution and environmental quality. However, due to the limited number of sites, it is increasingly difficult to meet the high spatial and temporal resolution needed for atmospheric environmental monitoring [7]. Thus, remote sensing has become an increasingly important source of information for managing and controlling straw burning due to its large-area, synchronous, and economical monitoring capabilities [8]. Fire detection with satellite remote sensing can be grouped in three categories: (1) active fire detection (thermal anomalies) [9], (2) the detection of fire effects on surface-burned area and burn severity from changes in spectral properties of surface following fire [10], and (3) the detection of smoke plumes, including quantitative measures, such as aerosol optical depth. This study focused on the detection of smoke plumes because smoke is an early product of straw burning and can be used as one of the important features for monitoring straw burning. With the development of deep learning techniques in recent years, significant progress has been made in computer vision. This study attempts to apply computer vision improved by the deep-learning algorithms to the field of fire detection. In computer vision, he morphological features of smoke are more easily captured than the spectral features of thermal anomalies [11]. Existing methods for detecting straw combustion mainly look for combustion products, especially smoke. Detecting straw combustion is a difficult task and has been a popular topic in the field of machine vision over the last few years [12]. To date, scholars have carried out extensive research on identifying smoke. In the early approaches, the research object was enhanced and displayed, mainly as a band synthetic image, which allowed manual extraction [13]. [PERSON] et al.