论文标题
基于深度学习的分类系统，用于识别本地菠菜
Deep Learning Based Classification System For Recognizing Local Spinach
论文作者
论文摘要
深度学习模型通过从训练有素的数据集中研究来为图像处理带来令人难以置信的结果。菠菜是含有维生素和养分的叶蔬菜。在我们的研究中，已经使用了一种可以自动识别菠菜的深度学习方法，并且该方法具有总共五种包含3785张图像的菠菜。四个卷积神经网络（CNN）模型用于对我们的菠菜进行分类。这些模型为图像分类提供了更准确的结果。在应用这些模型之前，图像数据进行了一些预处理。对于数据的预处理，需要进行一些方法。这些是RGB转换，过滤，调整和重新缩放以及分类。应用这些方法后，图像数据将进行预处理，并准备在分类器算法中使用。这些分类器的准确性在98.68％-99.79％之间。在这些型号中，VGG16的准确度最高为99.79％。
A deep learning model gives an incredible result for image processing by studying from the trained dataset. Spinach is a leaf vegetable that contains vitamins and nutrients. In our research, a Deep learning method has been used that can automatically identify spinach and this method has a dataset of a total of five species of spinach that contains 3785 images. Four Convolutional Neural Network (CNN) models were used to classify our spinach. These models give more accurate results for image classification. Before applying these models there is some preprocessing of the image data. For the preprocessing of data, some methods need to happen. Those are RGB conversion, filtering, resize & rescaling, and categorization. After applying these methods image data are pre-processed and ready to be used in the classifier algorithms. The accuracy of these classifiers is in between 98.68% - 99.79%. Among those models, VGG16 achieved the highest accuracy of 99.79%.
