摘 要
森林病虫害是威胁全球森林生态系统健康和可持续发展的重要因素，传统监测手段存在效率低、成本高及精度不足等问题。为此，本研究旨在探索机器学习算法在森林病虫害监测中的应用潜力，以提升监测的准确性和时效性。研究选取了多种典型机器学习算法，包括支持向量机、随机森林以及深度学习模型，并结合遥感影像和地面调查数据进行模型训练与验证。通过对比分析不同算法的性能表现，结果表明深度学习模型在复杂环境下的病虫害识别精度显著优于传统算法，且能够有效处理大规模、多源异构数据。此外，本研究提出了一种基于迁移学习的优化框架，解决了小样本条件下模型泛化能力不足的问题，为实际应用提供了技术支持。研究表明，机器学习技术可显著提高森林病虫害监测的智能化水平，为林业管理决策提供科学依据，其创新点在于将迁移学习引入森林病虫害监测领域，拓展了算法的适用范围，具有重要的理论价值和实践意义。关键词：森林病虫害监测; 机器学习; 深度学习; 迁移学习; 遥感影像
Abstract
Forest pests and diseases are significant factors threatening the health and sustainable development of global forest ecosystems. Traditional monitoring methods suffer from issues such as low efficiency, high cost, and insufficient accuracy. To address these challenges, this study explores the application potential of machine learning algorithms in forest pest and disease monitoring to enhance both accuracy and timeliness. A variety of typical machine learning algorithms, including support vector machines, random forests, and deep learning models, were selected and trained using remote sensing images and ground survey data for validation. Comparative analysis of the performance of different algorithms revealed that deep learning models demonstrated significantly higher accuracy in identifying pests and diseases in complex environments compared to traditional algorithms, while effectively handling large-scale, multi-source heterogeneous data. Furthermore, this study proposed an optimized fr amework based on transfer learning, addressing the issue of insufficient model generalization under small sample conditions and providing technical support for practical applications. The findings indicate that machine learning technologies can substantially improve the intelligence level of forest pest and disease monitoring, offering scientific evidence for forestry management decision-making. The innovation of this study lies in introducing transfer learning into the field of forest pest and disease monitoring, expanding the applicability of algorithms and demonstrating important theoretical and practical significance.Key words:Forest Pest And Disease Monitoring; Machine Learning; Deep Learning; Transfer Learning; Remote Sensing Images
目 录
摘 要 I
Abstract II
引 言 1
第1章、森林病虫害监测概述 3
1.1、森林病虫害现状分析 3
1.2、传统监测方法的局限性 3
1.3、机器学习引入的必要性 3
第2章、机器学习算法在监测中的应用基础 5
2.1、数据采集与预处理技术 5
2.2、常见机器学习算法介绍 5
2.3、算法选择与适用场景 6
第3章、机器学习在病虫害识别中的应用 7
3.1、图像识别技术的应用 7
3.2、特征提取与模式匹配 7
3.3、精度评估与优化策略 8
第4章、智能监测系统的构建与实践 9
4.1、监测系统架构设计 9
4.2、实时监测与预警机制 9
4.3、应用案例与效果分析 9
结 论 11
参考文献 12
