随着人工智能的迅猛发展, 深度学习技术已经渗透到各种不同领域。农业研究者们利用深度学习较强的图像和数据处理能力将其用于农业生产中, 尤其是在农业病虫害识别方面取得了显著的成果。运用基于深度学习的农业病虫害识别技术可以在各种复杂环境下对农作物进行快速无损识别, 并且准确性高, 稳定性好, 从而使农民可以迅速采取有效的防治措施, 最大程度的降低农作物的损失。本文首先阐释了基于深度学习的农业病虫害识别技术对农业发展的重大意义, 并分别对各类病虫害识别技术进行了详细的优劣势分析以及它们在病虫害识别中的表现。其次, 介绍了深度学习的各项关键技术, 包括数据源、数据预处理、数据增强、网络模型选择、迁移学习等多种核心技术的概念和应用情况。最后, 根据以上研究结果, 分析深度学习在农业病虫害识别中遇到的问题, 探讨了其在病虫害识别中的未来发展方向。

With the rapid development of artificial intelligence, deep learning technology has penetrated into a variety of different fields. Agricultural researchers have used deep learning's strong image and data processing capabilities to apply it to agricultural production, especially in the identification of agricultural pests and diseases, which has achieved remarkable results. The use of deep learning-based agricultural pest and disease identification technology can quickly and non-destructively identify crops in various complex environments, with high accuracy and good stability, so that farmers can quickly take effective control measures to minimize the loss of crops. This paper first explains the significance of deep learning-based agricultural pest identification technology to agricultural development, and makes a detailed analysis of the advantages and disadvantages of various pest identification technologies and their performance in pest and disease identification. Secondly, various key technologies of deep learning are introduced, including the concepts and applications of various core technologies such as data source, data preprocessing, data augmentation, network model selection, and transfer learning. Finally, based on the above research results, the problems encountered by deep learning in the identification of agricultural pests and diseases are analyzed, and its future development direction in the identification of pests and diseases is discussed.