智能化技术赋能复杂环境虫害检测研究

Research on Intelligent Technology-Enabled Pest Detection in Complex Environments

摘要: 复杂环境下的虫害检测长期面临环境干扰强、虫害特征复杂、区域覆盖难、数据处理低效等多重瓶颈，传统人工巡查与诱捕统计等方式已难以满足精准防控需求。本文采用系统性综述方法，系统梳理了计算机视觉、物联网、深度学习、大数据四项核心技术在复杂环境虫害检测中的核心应用形式与优化方向，精准剖析技术在抗干扰适配、成本推广、数据安全共享方面的应用瓶颈，并从技术、应用、数据管理三维度提出针对性优化策略。为复杂环境虫害检测的智能化、标准化、规模化发展提供理论与实践参考。

Abstract: Pest detection in complex environments has long faced multiple bottlenecks, including strong environmental interference, complex pest characteristics, difficulties in regional coverage, and inefficient data processing. Traditional methods such as manual inspection and trap statistics have become inadequate for precise prevention and control. This paper adopts a systematic review method to systematically sort out the core application forms and optimization directions of four core technologies: computer vision, the Internet of Things, deep learning, and big data, in pest detection in complex environments. It precisely analyzes the application bottlenecks of these technologies in terms of anti-interference adaptation, cost promotion, and data security sharing, and proposes targeted optimization strategies from the three dimensions of technology, application, and data management. This provides theoretical and practical references for the intelligent, standardized, and large-scale development of pest detection in complex environments.