Site-specific mechanical weeding robot with automatic self-adjustable individually controlled cultivator

Weeds significantly reduce crop productivity and profitability, particularly in sugar beet production where losses exceed $1.25 billion annually in the United States. Limitations of herbicide-based control and drawbacks of conventional tillage highlight the need for efficient, sustainable alternatives. To develop and evaluate an autonomous site-specific mechanical weeding (SSMW) system with adaptive control for precise inter-row weed removal under field conditions. An SSMW system was designed by integrating an inter-row tillage mechanism with a robotic platform using RTK-GPS for navigation and site-specific actuation. Site-specific weeding was performed in the field test and a strain-based feedback control system was implemented in computer simulation to enable ASIC tillage under uneven terrain. Simulation studies based on field data were conducted to validate control performance, followed by field experiments to assess system effectiveness. The SSMW system’s efficacy was found to be 96.5% by tillage length. The SSMW system achieved precise site-specific weeding using GPS-based control of individual sweeps, with a weeding efficacy of 96.5% based on tillage length. The ASIC framework with sensor feedback simulation showed the feasibility of improved adaptability to uneven terrain, with future work focusing on enhanced sensing and faster actuation for improved field performance. This study contributes to precision agriculture by enabling targeted weed management through intelligent sensing and control, reducing energy use and soil disruption. The system offers a sustainable alternative for organic farming, improving operational efficiency and environmental stewardship. Developed an autonomous site-specific mechanical weeding (SSMW) system integrated with a robotic platform for inter-row weed control. Implemented strain-based feedback control enabling automatic self-adjustable and individually controlled (ASIC) tillage on uneven terrain. Achieved high weed control efficacy (96.5%) based on tillage length under field conditions. Reduced unnecessary soil disturbance and tillage energy through targeted, site-specific operation. This work advances precision agriculture by enabling site-specific weed management using autonomous robotics and adaptive control. The proposed SSMW system aligns with precision agriculture principles by improving input efficiency, minimizing environmental impact, and enhancing sustainability in crop production systems.