Enhancing Weeding Efficiency: Addressing Targeting Positional Errors and Key Determinants of Cutting Efficiency in Laser Weeding Robots

Abstract

Laser weeding technology offers an effective alternative to traditional chemical and mechanical methods, providing precision, low cost, and environmental benefits. However, automatic targeting of weeds using lasers often encounters positional errors, particularly in dynamic weeding modes, which can significantly reduce weed removal efficiency. In addition, the operational efficiency of laser weeding is influenced by multiple factors, and the coupling effects of these factors require further investigation. This article examines the impact of laser power, incident angle, and spot size on the weeding efficiency of four common weed species under static conditions, considering the presence of positioning errors in laser targeting. To address these targeting errors, four weeding patterns were proposed: zigzag, triangular, horizontal, and vertical error compensation trajectories. Among these, the horizontal error compensation trajectory proved to be the most efficient, yielding stable and reliable results. In addition, a laser spot size adjustment device was designed to vary the spot diameter between 1–4 mm. Through four exploratory experiments and one validation experiment, the optimal combination of weeding parameters was identified: the horizontal weeding pattern, maximum laser power, an incidence angle of 80<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>, and a 2 mm spot diameter. This combination achieved optimal compensation with position errors under 2 mm. Validation experiments demonstrated that under these conditions, the average cutting times for chenopodium album, polygonum hydropiper, setaria viridis, and eleusine indica were 0.411 s, 0.308 s, 0.419 s, and 0.384 s, respectively, highlighting the efficiency and stability of this laser weeding model.