Abstract: Plant protection is moving toward mechanization and intelligence in the process of modern agriculture in recent years. Autonomous operation of unmanned aerial vehicles (UAV) can reduce the quality errors from the visual images or manual experience, particularly for labor cost saving. Among them, trajectory optimization is one of the most important steps during the autonomous operation of plant protection UAVs. A reasonable algorithm of trajectory optimization can be expected for the UAVs to more efficiently and safely realize the tracking operations. The current algorithm of mainstream optimization focuses mainly on trajectory accuracy and algorithm efficiency in the tracing scene. But there is an overshoot of state quantity and serious trajectory deviation in the special scene of agricultural plant protection, due to complex situations, such as line feed and turn. Therefore, it is very necessary to optimize the trajectory suitable for the operation scenarios of plant protection. Simple time allocation cannot fully meet the requirements of trajectory optimization in the traditional minimum-SNAP algorithm, such as the large trajectory deviation error. In this study, an improved minimum-SNAP algorithm was proposed for trajectory optimization in the operation scenarios of plant protection. Firstly, the real-time kinematic (RTK) integrated navigation system carried by the plant protection UAV was used to collect the topographic data of the plant protection plots. Then the data was imported into the robot operating system (ROS) in the central controller. The surface fitting was also utilized to obtain the three-dimensional spatial map. The cattle ploughing method was selected to plan the initial operation path. Secondly, the task path was optimized using an improved minimum-SNAP algorithm. The cost function of time allocation was also constructed using the state parameter variable of the initial operation path. In addition, the change rate of the initial path baseline angle was introduced as a reference factor, in order to obtain the optimal time allocation after optimization. Thirdly, the coefficient optimization cost function of the minimum-SNAP algorithm was reconstructed using the time allocation information. The gradient penalty factor of position offset was then added to the optimization function. The trajectory expression of the piecewise polynomial was then obtained to solve the coefficient of the trajectory polynomial after optimization. Finally, the UAV position control period was substituted into the piecewise trajectory polynomial for the track point, which was taken as the location expectation to control the UAV motion. A simulation test was carried out to preliminarily verify using the Matlab platform. The initial path was taken as the "U" type round-trip operation track in two-dimensional space. The average acceleration of the improved algorithm was 0.390 m/s², which was 19.75% lower than the traditional. Furthermore, the average trajectory offset was 0.051 m/s², which was 66.88% lower than the traditional. The physical test site was selected as the mulberry forest on the slope with the complex terrain. The test results show that the improved algorithm reduced the average acceleration and deviation by 7.82%, and 45.56%, respectively, compared with the traditional under the same running time. An outstanding inhibition was achieved in the trajectory deviation with the reduced overshoot during line feed turning. The finding can also provide a technical and theoretical basis for the UAV trajectory optimization in the scenario of plant protection.