Work

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

Year: 2026

Type: article

Abstract: Context 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. Aims To develop and evaluate an autonomous site-specific mechanical weeding (SSMW) system with adaptive control for precise inter-row weed removal under field conditions. Methods 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. Key Results The SSMW system’s efficacy was found to be 96.5% by tillage length. Conclusion 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. Implications and Impacts 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.

Source: Precision Agriculture

DOI: 10.1007/s11119-026-10445-3

Authors: Shafi Md. Istiak, Mohammad Aftabi Talami, James Y. Kim, Sulaymon Eshkabilov

Institutions: North Dakota State University, Edward T. Schafer Agricultural Research Center

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: hybrid

Estimated APC paid: $4,190