Optimizing UAV-based herbicide applications for sustainable wheat weed management by a novel multi-parameter field evaluation under variable environmental conditions

Unmanned aerial vehicles (UAVs) offer potential for precise, sustainable herbicide application in wheat, but their efficacy under variable field conditions requires robust evaluation. This study aims to rectify these research gaps and introduces a novel multi-parameter field evaluation of UAV-based herbicide applications for sustainable wheat weed management, explicitly evaluating the critical impact of real-time meteorological variability and application parameters. Field experiments over two growing seasons (2021/2022 and 2022/2023) compared UAV applications (30 L ha −1 , fine-to-medium droplets) at 1.5 m and 2.5 m altitudes with conventional spraying (200 L ha −1 , medium-coarse droplets), using constant doses of two commercial herbicide formulations (1. tritosulfuron + florasulam ; 2. iodosulfuron-methyl-sodium + amidosulfuron + mefenpyr-diethyl ). Efficacy was measured via species-specific weed density and fresh mass reduction. UAV treatments achieved significantly higher or equivalent suppression of Capsella bursa-pastoris , Lactuca serriola , Sinapis arvensis , and Veronica persica compared to conventional spraying. Despite more challenging conditions (high temperature, lower humidity, stronger wind) in the second season, overall efficacy increased, attributed to using a more effective herbicide formulation, underscoring the critical role of herbicide selection for UAV systems. Lamium purpureum exhibited significant weather sensitivity, with lower flight altitude enhancing fresh mass reduction. Correlation analysis suggested temperature positively (r = 0.586, p = 0.045) and wind velocity negatively (r = ̵ 0.588, p = 0.045) influenced treatment efficacy. UAV applications achieved up to 90 % efficacy in the second year by 31 DAHA (i.e., herbicide application) while using 85 % less water. This research provides the first multi-parameter field validation of UAV herbicide application under variable environmental conditions, demonstrating its viability and significant water-saving potential. The findings offer crucial, actionable inferences for optimizing UAV parameters (altitude, droplet size) with herbicide selection and real-time weather data, benefiting global precision agriculture efforts towards resource-efficient and environmentally responsible weed management.