## 1 Introduction  
Comparing with other pesticide applicators, unmanned aerial vehicles (UAV) can achieve accurate and specific management with its low costs, high efficiency, and mobility [1; 2]. UAV crop protection has the characteristics of strong field adaptability and controllable flight parameters, which can be suitable for different crop planting areas [3]. However, UAV spray will produce droplet drift when downwind movement of airborne spray occurs, and a fraction of the dosage does not reach the target area [4; 5]. Safety questions and pesticide waste induced by spray drift are the main problems in the UAV spray field [6]. When pesticides drift out of the operation area, it will cause people nearby to have diseases like cancers and allergies [7]. Droplet size is one of the most important characteristic parameters for spray and highly related to spray drift, especially the percentage of fine droplets [4]. The pest control efficiency, on-targetdeposition, and spray losses are all influenced by droplet size [8]. It is also very important to quantitatively analyze the droplet size distribution for studying the trajectory of the droplet and interaction between droplet and plant target interface [9]. Volume medium diameter (VMD) is one of the most commonly used parameters of the collection of droplets. The volume of all droplets are accumulated from small to large during spraying, VMD corresponds to droplet diameter when the droplet volume equals 50% of the total droplet [10]. Droplet deposition uniformity is also an important aspect of spray characterization [11]. In order to achieve effective diseases and pests control, researchers used many methods to measure the deposition during UAV spraying [12]. The nozzle is the most critical component of pesticide spray technology, which determines the droplet size and deposition distribution [13]. [PERSON] et al. [14] found that choosing the right nozzle to atomize the droplets with the right size can significantly reduce the drift. [PERSON] et al. [15] found that nozzle type had the most evident effect on the droplet size of the herbicide spray compared with orifice size, herbicide active ingredient, pressure, and carrier volume. [PERSON] et al. [16] studied the droplet deposition uniformity of different nozzles and found the condition of achieving the same spray characteristics of air induction nozzles with a conventional nozzle. Thus, it is of great significance to analyze and evaluate the droplet size and deposition distribution of nozzles. In recent years, researchers have carried out the analysis and modeling of the nozzle's droplet size and deposition distribution. [PERSON] et al. [17] developed a three-dimensional Lagrangian model to simulate the deposition patterns produced by twin-nozzle electrospray. [PERSON] et al. [18] established droplet size models for two brands of nozzles, and the two models showed a high degree of confidence after validated by the actual test method. [PERSON] et al. [19] studied the droplets deposition distribution by UAV spraying in the wind tunnel and obtained a prediction model. [PERSON] et al. [20] developed an integrated computational fluid dynamics (CFD) model to predict deposition from air-assisted sprayer onto the tree canopies and demonstrated the ability of this model to evaluate spray application performance. Machine learning is a branch of artificial intelligence, which can reduce the burden of computing and accelerate the modeling process. It reduces the complexity and improves the performance compared with the physical model [21]. Machine learning methods have been widely used in agriculture, energy engineering, biomedicine, and other fields [22; 23; 24]. As one kind of machine learning model, artificial neural network model can be well trained and proved to be an alternative numerical simulation technology of CFD under the spray cooling condition [25]. [PERSON] et al. [26] proposed a new approach based on machine learning of the deposition prediction in real time and proved its feasibility. [PERSON] et al.