Aiming to meet the operational requirements of agricultural plant protection UAVs and ensure their flight efficiency and safety, a hybrid improved sand cat swarm optimization (HISCSO) algorithm was proposed for UAV path planning. Firstly, a nonlinear control factor was designed to dynamically balance the transition between algorithmic phases. Secondly, the golden sine strategy was introduced during the attack phase to enhance the algorithm's local exploitation capability and accelerate convergence. Finally, by leveraging the strengths of a smooth exploration strategy, population diversity was maintained, and the algorithm's global optimization ability was improved. The performance of the algorithm was validated by using the CEC2022 benchmark suite. Experimental results showed that compared with the original algorithm and six other optimizers, HISCSO achieved the best performance on 75% of the test functions. The study formulated a cost function that satisfied multiple operational constraints and constructed UAV mission environments in hilly areas based on a digital elevation model map. Across four environments of varying complexity, HISCSO consistently located the globally optimal route, producing the smoothest and shortest path. Compared with the original algorithm, HISCSO improved stability by 10.21%, 36.59%, 29.27% and 46.46% across the four representative agricultural scenarios, demonstrating that it simultaneously possessed global search capacity and local smoothness preservation, and thus offered a highly reliable path planning solution for low altitude plant protection UAV operations.