System Design of an Intelligent Weeding Robot Based on Machine Vision for Weed Recognition
基于机器视觉的杂草识别智能除草机器人系统设计

摘要

面对传统农业除草方式效率低下、化学残留严重及劳动力成本高昂的严峻挑战，构建基于机器视觉的智能除草机 器人系统成为现代农业装备发展的必然趋势。本文深入剖析了机器视觉在复杂田间环境下的杂草识别机理，提出了一 种融合多光谱成像与深度卷积神经网络的自适应识别架构。该系统通过优化图像预处理算法增强特征提取能力，利用 改进型目标检测模型实现作物与杂草的精准区分，并设计了一套基于实时路径规划的机械臂执行策略。研究重点解决 了光照变化、叶片遮挡及背景噪声干扰等关键技术难题，显著提升了识别准确率与作业鲁棒性。实验结果表明，该设 计方案有效降低了农药使用量，实现了非接触式精准除草，为智慧农业的自动化作业提供了具有理论支撑与实践价值 的技术路径。

Abstract

 In response to the serious challenges of low efficiency, severe chemical residues, and high labor costs in traditional agricultural weeding methods, the construction of an intelligent weeding robot system based on machine vision has become an inevitable trend in the development of modern agricultural equipment. This paper provides an in-depth analysis of the weed recognition mechanism of machine vision in complex field environments and proposes an adaptive recognition architecture integrating multispectral imaging and deep convolutional neural networks. By optimizing image preprocessing algorithms, the system enhances feature extraction capability, uses an improved object detection model to accurately distinguish crops from weeds, and designs a robotic arm execution strategy based on real-time path planning. The study focuses on solving key technical problems such as illumination variation, leaf occlusion, and background noise interference, thereby significantly improving recognition accuracy and operational robustness. Experimental results show that the proposed design effectively reduces pesticide use and realizes non-contact precision weeding, providing a technically feasible path with both theoretical support and practical value for automated operations in smart agriculture.