The research proposes an integrated methodology for vehicle navigation and motion analysis using computer vision and path planning techniques that ensures the automation of mechanical and labor-oriented task of hand weed management. To reduce agricultural dependency on herbicide, improving its sustainability and reducing its environmental impact, this research will show an innovative pathway to work as feature detection, visual odometry, Inertial Measurement Unit (IMU) integration, Differential Global Positioning System (DGPS) fusion, 3D mapping, and Particle Swarm Optimization (PSO)-based path planning are the main Key components. A mobile robot is designed to provide the best path solution for Uttarakhand’s hilly terrain by making use of robotic spraying technology combined with enhanced manual functions. To improve vehicle localization accuracy, optimize navigation courses, and ease obstacle avoidance in dynamic situations, the information is taken from previous and future frames. By mapping coordinate positions within the camera’s coordinate system and employing PSO-based path planning, the system ensures accurate decision-making to find the optimal route for weed spraying.