智能精准对靶除草技术与装备研究进展

Research Progress of Technologies and Equipment for Intelligent and Accurate Target Weeding

摘要:

传统大规模化学除草导致农药浪费和环境污染加剧，非精准机械除草存在伤苗率高、效率低等问题，基于图像识别的激光除草等智能精准对靶除草技术成为实现农业绿色可持续发展的关键路径。本文阐述了相关技术“感知-控制-执行”的工作机制与核心模块，结合国内外最新研究进展与典型装备案例，重点剖析了杂草识别、杂草定位、对靶控制与部件执行等关键技术环节。研究结果表明，基于高精度视觉感知与智能决策算法能实现杂草精准识别与定位，并通过激光、机械或喷雾等末端执行器完成高效对靶作业，从而显著降低除草剂依赖、减少作物误伤、提升农田经济效益与生态效益。然而，该技术仍面临专用部件研发滞后、抗干扰与信息融合深度不足、装备成本高昂、技术路线单一化等挑战，制约其规模化应用。同时阐述了未来研究应着力突破的方向，如专用部件设计、提升系统鲁棒性、深化多技术融合、拓展新兴场景应用，以推动智能精准对靶除草产业化发展。

Abstract:

Traditional large-scale chemical weeding causes pesticide waste and intensifying environmental pollution, while non-precision mechanical weeding is plagued by defects, including high seedling injury rate and low operation efficiency. Intelligent precision targeted weeding technologies, represented by image recognition-based laser weeding, have thus become a critical pathway for realizing green and sustainable agricultural development. It expounded the "perception-control-actuation" working mechanism and core modules of relevant technologies, and an in-depth analysis of key technical links covering weed identification, weed positioning, targeted control and end-effector actuation was conducted, with reference to the latest domestic and overseas research progress and typical equipment cases. Research findings indicated that high-precision visual perception and intelligent decision-making algorithms supported accurate weed identification and positioning;end effectors of laser, mechanical or spraying types can complete high-efficiency targeted operations. This technical framework can significantly reduce herbicide reliance, lower accidental crop damage, and enhance the economic and ecological benefits of farmland. Nevertheless, the technology was still confronted with challenges such as lagged R&D of dedicated components, deficient anti-interference capacity and insufficient depth of information fusion, high equipment costs, and singular technical routes, which constrained its large-scale application. Furthermore, it clarified priority breakthrough directions for future research, including dedicated component design, system robustness improvement, in-depth multi-technology integration, and expansion of emerging application scenarios, to drive the industrialization of intelligent precision targeted weeding.