深度学习方法在草莓病虫害防治中的应用现状与展望

The Application Status and Prospects of Deep Learning Methods in Strawberry Disease and Pest Control

草莓果肉鲜美,营养价值高,素有“水果皇后”的美誉,然而,草莓的生长特性使其容易受到多种病虫害的影响;因此,病虫害的识别和防治对于保证草莓品质和产量至关重要。病虫害识别是病虫害防治的前提,深度学习方法因其具有可以自动学习并提取特征的特点而被越来越多地与图像识别技术结合并应用于农作物病虫害识别。基于此,介绍了基于深度学习的草莓病害识别流程,综述了近年来深度学习方法在草莓病虫害识别方面的研究进展,指出深度学习方法应用于草莓病虫害防治具有高效率、鲁棒性好和泛化能力强的优势,但也存在计算资源要求高、数据标注主观性和实际应用场景有限等局限性。在未来通过创建数据集共享平台,采用模型压缩技术,以及将深度学习与物联网、机器人等技术更紧密结合,将进一步提升应用效果、降低防治成本,助力绿色防控。

Strawberries have delicious flesh and high nutritional value,earning the the reputation of the 'Queen of Fruits'. However, the growth characteristics of strawberries make them susceptible to various pests and diseases. Therefore, identifying and controlling pests and diseases are crucial for ensuring the quality and yield of strawberries. Disease and pest identification is a prerequisite for pest control, and deep learning methods are increasingly being combined with image recognition technology for crop disease and pest identification due to their ability to automatically learn and extract features. Based on the above all, the process of strawberry disease recognition based on deep learning was introduced, and the research progress of deep learning methods in strawberry disease and pest recognition in recent years was summarized. It pointed out that deep learning methods had the advantages of high efficiency, good robustness and strong generalization ability in strawberry disease and pest control. However, there were also limitations, such as high computational resource requirements, subjective data annotation, limited practical application scenarios and so on. In the future, it will further improve application effectiveness, reduce prevention and control costs, and assist in green prevention and control by creating a dataset sharing platform, adopting mode compression technology and combining deep learning with technologies such as the Internet of things and robotics more closely.