农作物病虫害识别关键技术研究综述

农作物病虫害的预防与治理对农业生产具有十分重要的作用，病虫害防治工作的前提是准确识别病虫害目标。传统的病虫害识别方法包括人工识别和仪器识别，传统识别方法在识别效率、识别准确性、应用场景等方面已无法满足科学研究和生产的需要。深度学习是机器学习的一个重要分支，能够自动、高效、准确地从大规模数据集中学习到待识别目标的特征，从而替代传统依赖手工提取图像底层特征的识别方法，因此，将结合图像处理的深度学习技术应用于农作物病虫害识别是未来精准农业发展的必然趋势。农作物病虫害识别所涉及的关键技术以农作物病虫害数据为基础展开，通过阐述病虫害数据获取、数据预处理、数据增强、深度学习网络优化、识别结果可视化、识别结果可解释性、预测预报等关键技术的研究现状，归纳与总结了各关键技术应用中存在的问题和面临的挑战，最后指出农作物病虫害识别未来的研究发展方向，即在数据获取方面，构建多源农业数据集和积极打造数据共享资源平台，在数据处理方面，结合迁移学习算法、使用新型数据增强方法，在数据应用方面，积极开展可视化、可解释性和预测预报等工作。

Review of Key Techniques for Crop Disease and Pest Detection

Preventing and managing crop disease and pest has significant impacts on agricultural production. The prerequisite for disease and pest control is accurate detection. Traditional crop disease and pest detection methods rely on human labors and instructions. However, these methods can no longer meet the requirements of scientific research and production, such as detection efficiency, accuracy, and application scenarios. As a main stream of machine learning, deep learning can extract features of objects from large-scale datasets automatically and efficiently, thereby releasing traditional methods from manual feature extraction. Applying deep learning, combined with image processing techniques, to detect crop disease and pest becomes an inevitable trend of precision agriculture in the future. The key techniques in crop disease and pest detection depend on agricultural data. After reviewing the state of the art of key techniques in this domain, including data acquisition, data pre-processing, data augmentation, deep learning network optimization, data visualization, and explainability of results, the challenges of applying these key techniques were detected and summarized. Lastly, potential solutions were explored to highlight the future research lines in this domain, including defining multi-view agricultural datasets, combining transfer learning, adopting new data augmentation methods, and considering visualization and explanation issues.