The Synergy of Agricultural Open Space and Drone Based on Color Difference and Gradient Characteristics and Sensor Integration

Aiming to address the issue of air–ground coordination in agriculture, a model of a four-rotor unmanned aerial vehicle (UAV) and a two-wheel differential unmanned vehicle was developed. This paper proposes a visual tracking and sensor integration method for agricultural UAVs based on color difference and gradient characteristics, which is applied to facilitate air–ground cooperative agricultural operations for accurate identification and tracking of ground targets. The method utilizes the vision sensor carried by the UAV, leveraging color difference and gradient characteristics in conjunction with the OTSU method for image segmentation. This effectively enhances the UAV’s ability to track specific targets in diverse environments and improves the flexibility of drones and the precise monitoring capabilities of sensors. The experiment encompasses three scenarios: Indoor, outdoor, and interforest. Results indicate that the error in indoor tracking experiments is within 0.5[Formula: see text]m, while outdoor tracking experiments yield errors within 0.7[Formula: see text]m, and interforest tracking experiments result in errors within 0.8[Formula: see text]m. The method demonstrates effective tracking capabilities across various environments such as indoor settings, outdoor areas, and interforest regions. It successfully mitigates external factors’ interference, including changes in light conditions, shadows, and occlusions; thereby providing technical support for precision agriculture applications involving agricultural UAVs. It improves the accuracy and efficiency of monitoring and provides a scientific basis for decision-making in all walks of life.