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防治农作物病虫害是农业生产中的一大难题，及时准确地识别病虫害对于维护农业生产的稳定和环境的保护至关重要。常见的农作物病虫害识别手段包括人工识别、显微镜观察和分析、生物学检测、计算机视觉等，其中，计算机视觉在病虫害识别领域中逐渐成为一种重要的手段，具有高效、快速、准确的特点。阐述传统病虫害识别模型的原理和缺点，简要介绍基于卷积神经网络与迁移学习的病虫害识别模型的基本流程，对国内外一些相关文献进行了总结，并对目前农作物病虫害识别模型提出了存在的问题和改进方向。
Abstract：The prevention and control of crop disease and pest is a major problem in agricultural production. Timely and accurate recognition of pests is very important for maintaining the stability of agricultural production and protecting the environment.Common methods of crop disease and pest recognition include manual recognition, microscope observation and analysis, biological detection, computer vision, etc. Among them, computer vision has gradually become an important means in the field of pest recognition, which has the characteristics of high efficiency, fast and accurate.This paper describes the principle and shortcomings of the traditional pest recognition model, briefly introduces the basic process of the pest recognition model based on convolutional neural network and transfer learning, summarizes some relevant literatures at home and abroad, and puts forward the existing problems and improvement directions of the current crop disease and pest recognition model.
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基本信息:
中图分类号:TP391.41;S43
引用信息:
[1]胡雯婧,余俊龙,梁雷,等.基于计算机视觉的农作物病虫害识别研究综述[J].微型电脑应用,2025,41(03):90-93+97.
基金信息:
临港新片区高新产业和科技创新专项项目(SH-LG-GK-2020-02-11)
2025-03-20
2025-03-20