摘要
精准农业是现代农业发展的重要方向,高效杂草识别是实现精准植保的关键环节。无人机凭借灵活高效、高时空分辨率的农田信息获取能力,成为该领域核心载体,但图像采集与预处理技术的不足制约其应用效果。本文从图像采集硬件与飞行控制算法、预处理算法优化两个维度系统梳理了田间图像获取与处理技术的研究现状,提出多光谱-可见光协同感知、传统与深度学习融合等技术发展方向,并建议强化轻量化机载平台研发、构建标准农田数据集、建立算法-硬件协同机制、制定农业数据安全规范,以期提升无人机精准植保应用水平。
Precision agriculture represents a crucial direction for modern agricultural development,and efficient weed identification is a key component in achieving precision plant protection.Unmanned aerial vehicle(UAV)platform has become the core vehicles in this field due to it flexible,efficient,and high spatial-temporal resolution capabilities for acquiring farmland information.However,limitations in image acquisition and preprocessing technologies constrain its application effectiveness.This paper systematically reviewed the current research status of field image acquisition and processing technologies from two dimensions,including image capture hardware and flight control algorithms,as well as optimization of preprocessing algorithms.The development directions such as multispectral-visible light collaborative sensing and the integration of traditional and deep learning techniques were proposed.Recommendations were put forward to enhance the development of lightweight airborne platforms,establish standardized agricultural field datasets,create algorithm-hardware coordination mechanisms,and formulate agricultural data security standards.Ultimately enhance the application level of precision plant protection using drones.
作者
仇嘉慧
胡波
Qiu Jiahui;Hu Bo(College of Automation,Guangxi University of Science and Technology,Liuzhou 545616,Guangxi,China)
出处
《新疆农机化》
2025年第6期55-60,共6页
Xinjiang Agricultural Mechanization
关键词
无人机遥感
杂草识别
图像采集
图像预处理
深度学习
精准农业
UAV remote sensing
Weed identification
Image acquisition
Image preprocessing
Deep learning
Precision agriculture
