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首页> 外文会议>International Conference on Image, Vision and Computing >Flower Species Recognition Based on the Convolutional Neural Network
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Flower Species Recognition Based on the Convolutional Neural Network

机译：Flower Species Recognition Based on the Convolutional Neural Network

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摘要

At present, in botany, agronomy, and species research, if the identification and classification of flowers is only done manually, it may require a lot of manpower and the recognition accuracy rate is low. Moreover, traditional computer vision and artificial intelligence are widely used. The search query in the database also faces some problems of high recognition cost, low recognition rate and low efficiency. In response to these problems, this article uses five common flower image data sets based on the deep learning field and image information processing problems. Based on the convolutional neural network framework, the flower processing is divided into the following four processes: image information import, preprocessing, Feature extraction and classification of image information. Through the training and verification of five kinds of flower data set graphics, the recognition accuracy rate of this model reaches 75%, and the recognition accuracy rate is improved compared with traditional recognition methods.
机译：目前，在植物学、农学和物种研究中，如果只进行人工识别和分类，可能需要大量人力，识别准确率低。此外，传统的计算机视觉和人工智能被广泛应用。数据库中的搜索查询也面临识别成本高、识别率低、效率低等问题。针对这些问题，本文基于深度学习领域和图像信息处理问题，使用了五种常见的花卉图像数据集。基于卷积神经网络框架，将花卉图像处理分为以下四个过程：图像信息导入、预处理、特征提取和图像信息分类。通过对五种花卉数据集图形的训练和验证，该模型的识别准确率达到75%，与传统的识别方法相比，识别准确率有所提高。

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