图像识别技术在农业病虫害监测中的实践与应用

Practice and Application of Image Recognition Technology in Agricultural Pest and Disease Monitoring

摘要: 随着农业现代化进程的加快，病虫害监测作为保障农作物产量与质量的关键环节，正逐步向智能化、精准化方向转型。图像识别技术凭借其非接触式、高效性和实时性优势，在农业病虫害监测领域展现出广阔应用前景。本文系统梳理该技术的核心原理与发展脉络，聚焦小麦条锈病无人机监测、番茄晚疫病温室监测两大代表性场景，深度剖析各场景下的图像采集方案、预处理方法、模型架构、性能评估标准及实际部署成效，通过横向对比传统图像处理、浅层机器学习与深度学习在处理效率、识别精度、成本投入及环境适应性等维度的差异，提炼不同技术路线的适用边界与优劣特征。针对当前技术应用中图像质量不稳定、背景干扰显著、模型泛化能力弱及应用成本高等问题，从技术优化、数据建设、成本控制与应用模式创新四个层面提出解决路径，并构建基于场景需求与资源条件的技术选型框架，为推动图像识别技术在农业病虫害监测中的深度落地提供理论参考与实践借鉴，助力农业绿色可持续发展。

Abstract: With the acceleration of agricultural modernization, pest and disease monitoring, as a key link to ensure crop yield and quality, is gradually transforming towards intelligence and precision. Image recognition technology has shown broad application prospects in the field of agricultural pest and disease monitoring due to its advantages of non-contact, high efficiency and real-time performance. This paper systematically sorts out the core principles and development context of the technology, focuses on two representative scenarios: UAV monitoring of wheat stripe rust and greenhouse monitoring of tomato late blight, and deeply analyzes the image acquisition scheme, preprocessing method, model architecture, performance evaluation standard and actual deployment effect in each scenario. By horizontally comparing the differences between traditional image processing, shallow machine learning and deep learning in terms of processing efficiency, recognition accuracy, cost input and environmental adaptability, the applicable boundaries, advantages and disadvantages of different technical routes are summarized. Aiming at the current problems in technology application such as unstable image quality, significant background interference, weak model generalization ability and high application cost, solutions are proposed from four aspects: technical optimization, data construction, cost control and application mode innovation. A technical selection framework based on scenario needs and resource conditions is constructed to provide theoretical reference and practical reference for promoting the in-depth application of image recognition technology in agricultural pest and disease monitoring, and help the green and sustainable development of agriculture.