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基于深度学习的草原植物识别技术

Grassland plant image recognition technology based on deep learning

  • 摘要: 草原植物图像识别、分类是智慧林草的一个重要分支。近些年,随着大数据的快速发展,深度学习技术被越来越多的学者研究和使用。基于深度学习的草原植物识别受到相关领域学者和技术人员的关注和研究。综述了卷积神经网络、深度信念神经网络、递归神经网络、堆叠自编码器等四种基于深度学习网络模型及其在草原植物识别中的应用,介绍了基于深度学习的草原植物识别的技术路线、实现途径、面临的挑战及发展趋势,以期为相关研究和实践提供理论支撑。

     

    Abstract: Grassland plant image recognition technology is an important branch of intelligent forestry and grassland. In recent years, with the rapid development of Big Data, deep learning technology has been studied and used by more and more researchers. Plant image recognition and classification based on deep learning has attracted attention and research from scholars and technicians in related fields. This article reviewed four deep learning network models, such as convolutional neural network theory, deep belief network theory, recurrent neural network theory, and stacked autoencoder theory and their application in grassland plant recognition. and introduced the technical approaches, implementation methods, challenges, and development trends associated with deep learningbased recognition of grassland plants, aiming to offer theoretical support for relevant research and practical applications.

     

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