摘 要
随着农业现代化的快速发展，病虫害监测作为保障粮食安全和生态可持续发展的重要环节，其精准性和时效性要求日益提高。传统监测手段存在效率低、覆盖范围有限及数据获取滞后等问题，而无人机遥感技术凭借其灵活性强、分辨率高和实时性强的优势，为农业病虫害监测提供了新的解决方案。本研究旨在探索无人机遥感技术在农业病虫害监测中的应用潜力，通过结合多光谱与高光谱成像技术，构建基于机器学习算法的病虫害识别与评估模型。研究选取典型农作物种植区为试验对象，利用无人机搭载多源传感器采集作物冠层反射率、植被指数等关键参数，并通过数据预处理、特征提取及分类建模完成病虫害的空间分布与严重程度分析。结果表明，该方法能够以较高的精度实现病虫害的早期识别与动态监测，相较于传统方法显著提升了监测效率和准确性。此外，本研究创新性地引入了时间序列分析与空间异质性校正技术，有效解决了复杂农田环境下的数据干扰问题，进一步提高了模型的鲁棒性和适用性。总体而言，本研究不仅验证了无人机遥感技术在农业病虫害监测中的可行性与优越性，还为智慧农业的发展提供了重要的技术支持与理论参考。
关键词：无人机遥感技术；病虫害监测；多光谱与高光谱成像
ABSTRACT
With the rapid development of agricultural modernization, pest and disease monitoring, as a critical component for ensuring food security and ecological sustainability, is facing increasingly higher requirements in terms of accuracy and timeliness. Conventional monitoring methods are often constrained by low efficiency, limited coverage, and delayed data acquisition. In contrast, unmanned aerial vehicle (UAV)-based remote sensing technology, characterized by its high flexibility, fine resolution, and strong real-time capability, offers a novel solution for agricultural pest and disease surveillance. This study aims to explore the application potential of UAV remote sensing technology in pest and disease monitoring by integrating multispectral and hyperspectral imaging techniques to construct a machine learning-based identification and assessment model for crop diseases and pests. A typical crop planting area was selected as the experimental subject, where UAVs equipped with multi-source sensors were used to collect key parameters such as canopy reflectance and vegetation indices. Through data preprocessing, feature extraction, and classification modeling, the spatial distribution and severity of pest and disease infestations were analyzed. The results demonstrate that this approach can achieve early detection and dynamic monitoring of crop pests and diseases with high precision, significantly improving the efficiency and accuracy of monitoring compared to traditional methods. Furthermore, this study innovatively incorporates time-series analysis and spatial heterogeneity correction techniques, effectively addressing data interference issues in complex farmland environments and enhancing the robustness and applicability of the model. Overall, this research not only verifies the feasibility and superiority of UAV remote sensing technology in agricultural pest and disease monitoring but also provides essential technical support and theoretical references for the advancement of smart agriculture.
Keywords: Unmanned Aerial Vehicle Remote Sensing Technology; Pest And Disease Monitoring; Multispectral And Hyperspectral Imaging
目 录
摘 要 I
ABSTRACT II
第1章 绪论 1
1.1 农业病虫害监测的研究背景 1
1.2 无人机遥感技术的应用意义 1
1.3 国内外研究现状分析 2
1.4 本文研究方法与技术路线 2
第2章 无人机遥感技术基础 3
2.1 无人机遥感系统组成与原理 3
2.2 遥感数据获取与处理方法 3
2.3 多光谱与高光谱成像技术 4
2.4 数据质量评估与误差分析 4
2.5 技术优势与局限性 5
第3章 病虫害监测关键技术 6
3.1 病虫害特征提取与识别 6
3.2 基于遥感影像的植被指数分析 6
3.3 时间序列数据分析方法 7
3.4 模型构建与验证过程 7
3.5 监测精度提升策略 8
第4章 实际应用案例研究 9
4.1 不同作物病虫害监测对比 9
4.2 典型区域监测效果分析 9
4.3 数据驱动的决策支持系统 10
4.4 成本效益分析与优化建议 10
4.5 应用推广中的挑战与对策 11
结论 12
参考文献 13
致 谢 14
