Deep Learning Approaches for Cabbage Disease Classification

Sanjida Sultana Reya, Md Abdul Malek, Anik Debnath
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引用次数: 0

Abstract

Cabbage diseases such as black rot, downy mildew, and white rust are frequent and have a negative impact on yield. However, existing research lacks an accurate and rapid detector of cabbage diseases to assure healthy cabbage production. In this research, the transfer learning approach has been employed for many state-of-the-art CNN architectures, such as VGG16, VGG19, mobilnetv2, and InceptionV3, to determine the most optimal solution for this problem. A dataset of around 1500 images from three different classes is employed to train and validate the models. Among the multiple CNN models evaluated, vgg16 produced 95.55% test accuracy, which is far superior to other similar experiments conducted recently.
白菜病害分类的深度学习方法
白菜病害如黑腐病、霜霉病和白锈病频繁发生,并对产量产生负面影响。然而,现有的研究缺乏一种准确、快速的白菜病害检测方法,以保证白菜的健康生产。在本研究中,迁移学习方法已被用于许多最先进的CNN架构,如VGG16、VGG19、mobilnetv2和InceptionV3,以确定该问题的最优解决方案。使用来自三个不同类别的大约1500张图像的数据集来训练和验证模型。在评估的多个CNN模型中,vgg16的测试准确率为95.55%,远远优于近期进行的其他类似实验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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