TY -的A2 - Lv, Zhihan AU - Yu Yuanhui PY - 2021 DA - 2021/10/14 TI -作物病害图像识别的研究进展基于无线网络通信和深度学习SP - 7577349六世- 2021 AB -传统的数字图像处理技术有其局限性。它需要手动设计特性,消耗人力和物质资源,与单一类型和识别作物,结果是坏的。因此,找到一种有效的疾病和快速实时图像识别方法非常有意义。深入学习是机器学习算法,可以自动学习代表特性在图像识别领域取得更好的结果。因此,本文的目的是使用深度学习的方法来识别作物害虫和疾病,并找到有效和快速的实时图像识别方法。深入学习是近年来新开发的学科。它的目的是研究如何积极地获得各种特性表征方法从数据样本和依赖于数据驱动方法,一系列的非线性转换应用于最后收集原始数据从具体到抽象,从通用指定语义,从低级到高级的形式特征。本文分析了经典和最新的神经网络结构理论的基础上深入学习。为网络基于自然图像分类的问题不适合农作物病虫害识别任务,本文改进了网络结构,可以同时照顾识别速度和识别精度。我们讨论了农作物病虫害的影响特征提取对识别性能的层。 Finally, we used the inner layer as the main structure to be the pest and disease feature extraction layer by comparing the advantages and disadvantages of the inner and global average pooling layers. We analyze various loss functions such as Softmax Loss, Center Loss, and Angular Softmax Loss for pest identification. In view of the shortcomings of difficulty in loss function training, convergence, and operation, making the distance between pests and diseases smaller and the distance between classes more greater improved the loss function and introduced techniques such as feature normalization and weight normalization. The experimental results show that the method can effectively enhance the characteristic expression ability of pests and diseases and thus improve the recognition rate of pests and diseases. Moreover, the method makes the pest identification network training simpler and can improve the pest and disease recognition rate better. SN - 1530-8669 UR - https://doi.org/10.1155/2021/7577349 DO - 10.1155/2021/7577349 JF - Wireless Communications and Mobile Computing PB - Hindawi KW - ER -