Abstract: Abstract: Plant protection drones can rotate at high speed in the process of droplet spraying. The downwash flow field can be generated by the rotors, leading to droplet drift. A rapid and accurate prediction of the velocity in the downwash flow field under the rotor can greatly contribute to improving the effectiveness of the UAV's precision application. In this study, a prediction model was constructed in the downwash flow field of the single-rotor plant protection UAV using a physics-informed neural network. The prediction model effectively combined fluid dynamics and artificial intelligence (AI). A neural network model also incorporated into the physical equations. The powerful capabilities of the neural network were combined with the disciplinary context. Firstly, a physical model was used with the Lattice-Boltzmann to numerically simulate the flow field of the single-rotor plant protection UAV. The low-resolution flow field was then used to train the prediction model after numerical simulation. Secondly, the Navier-Stokes equations were embedded as the physics loss term in the prediction model, according to the fully connected neural network structure. The physics equations were utilized in the prediction model to learn the fluid flow patterns in the flow field. The interpretability of the model was enhanced to reduce the data dependence of the network model. Thirdly, the trainable parameters were updated iteratively to minimize the loss function during the training. The loss function was composed of both the physics and data loss terms. The training process was then realized to obtain the mapping relationship between physical quantities (such as velocity information) and space-time coordinates. As such, the mapping relationship was used to realize the fast prediction of the downwash flow field in the single-rotor plant protection UAV. Finally, the wind tunnel experiment was carried out to measure the velocity information of the flow field of the single-rotor plant protection drone under different side wind speed conditions. The accuracy and feasibility of the prediction model were verified to compare the differences between the experimental and predicted data. There was a small difference between the predicted and experimental values without the side wind. The errors between the predicted and experimental values were less than 0.6 m/s at four distances (including 0.3, 0.7, 1.1, and 1.5 m) below the rotor. The relative errors were within 15% at the rest of the locations, except that the low-velocity area was susceptible to external factors (such as ambient wind). The linear regression was performed on the predicted and experimental values in each directional velocity under the different conditions of side wind speed (including 0, 2, 4, and 6 m/s). The expressions of the fitted curves were y=0.949 27x+0.212 74 and y=0.941 76x-0.079 38, respectively. In conclusion, the prediction model can greatly contribute to determining the downwash flow field in the single-rotor plant protection drone. An effective technical reference can be offered to rapidly and accurately predict the flow of field information. The finding can also provide data support to the influencing mechanism of wind field on the droplet deposition distribution.