基于无人机技术的农田病虫害监测方法 -农学专业

摘要

随着精准农业的发展，传统农田病虫害监测方法在效率和精度方面已难以满足现代农业需求。本研究针对这一问题，提出了一种基于无人机技术的农田病虫害智能监测方法，旨在提高监测效率、降低人工成本并实现精准防控。研究采用多旋翼无人机搭载高光谱成像仪和可见光相机，通过自主设计的飞行路径规划算法对目标农田进行全方位数据采集。在数据处理环节，创新性地构建了基于深度学习的病虫害识别模型，该模型融合了卷积神经网络和注意力机制，能够有效提取作物病害特征并进行精确分类。实验结果表明，该方法在典型农作物上的病虫害识别准确率达到92.3%，较传统方法提升约15个百分点；单次作业面积可达50公顷，效率提升近10倍。同时，系统可生成病虫害分布热力图，为精准施药提供科学依据。

关键词：无人机技术；农田病虫害监测；深度学习；精准农业

MONITORING METHOD OF AGRICULTURAL DISEASES AND PESTS BASED ON UAV TECHNOLOGY

ABSTRACT

With the development of precision agriculture, the traditional methods of monitoring farmland pests and diseases have been difficult to meet the needs of modern agriculture in terms of efficiency and accuracy. To solve this problem, this study proposed an intelligent monitoring method of agricultural diseases and pests based on drone technology, aiming at improving monitoring efficiency, reducing labor costs and realizing accurate prevention and control. In this paper, a multi-rotor UAV equipped with hyperspectral imager and visible light camera is used to carry out comprehensive data acquisition of target farmland through self-designed flight path planning algorithm. In the process of data processing, a deep learn-based pest and disease recognition model was innovatively constructed, which integrated convolutional neural network and attention mechanism, and could effectively extract crop disease features and accurately classify them. The experimental results showed that the accuracy of the proposed method was 92.3%, which was about 15 percentage points higher than the traditional method. The area of a single operation can reach 50 hectares, and the efficiency is improved by nearly 10 times. At the same time, the system can generate thermal maps of the distribution of diseases and pests, providing scientific basis for precise drug application.

KEY WORDS：UAV technology; Farm pest monitoring; Deep learning; Precision agriculture

摘要 I

ABSTRACT II

第1章绪论 2

1.1 研究背景及意义 2

1.2 国内外研究现状 2

第2章无人机农田监测系统构建 3

2.1 无人机平台选型与配置 3

2.2 多光谱成像传感器集成 3

2.3 数据采集与传输系统设计 4

第3章病虫害识别算法研究 5

3.1 基于深度学习的特征提取方法 5