通过CNN的方法对真菌属进行分类

Ummi Sri Rahmadhani, Noveri Lysbetti Marpaung
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引用次数: 2

摘要

蘑菇是一种没有真正根和叶的植物。世界上已经发现的蘑菇有很多种,形状、大小和颜色各不相同。蘑菇在经济、健康等方面有很多好处。在印度尼西亚,蘑菇的好处之一是作为一种食物来源,但并不是所有类型的蘑菇都可以食用。为了识别蘑菇的种类,可以使用属和种的概念。属的概念被认为更容易,因为它根据相似的形态特征对蘑菇类型进行分组。因此,需要一种基于可消耗属和有毒属的蘑菇分类模型。由于卷积神经网络(CNN)在图像识别中具有良好的预测效果,因此本研究使用的方法是卷积神经网络(CNN)。该模型采用了三个卷积层、三个MaxPooling层和两个dropout层。dropout的使用旨在减少模型中的过拟合。本研究使用1200张图像的数据集,训练数据和测试数据的比例为70:30,得到840张训练数据和360张测试数据。该模型的最佳训练准确率为89%,验证准确率为82%。因此,可以得出结论,该模型能够使用CNN方法基于Genus对蘑菇进行分类
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Klasifikasi Jamur Berdasarkan Genus Dengan Menggunakan Metode CNN
Mushrooms are plants that do not have true roots and leaves. There are many types of mushrooms that have been identified worldwide, with various shapes, sizes, and colors. Mushrooms have many benefits in the fields of economy, health, and others. One of the benefits of mushrooms is as a food source in Indonesia, but not all types can be consumed. To identify mushroom species, the concepts of Genus and species can be used. The concept of Genus is considered easier because it groups mushroom types based on similar morphological characteristics. Therefore, a model is needed to classify mushrooms based on consumable and toxic genera. The method used in this research is Convolution Neural Network (CNN) due to its good predictive results in image recognition. The model in the research utilizes three convolution layers, three MaxPooling layers, and two dropout layers. The use of dropout aims to reduce overfitting in the model. The research uses a dataset of 1200 images with a training and testing data ratio of 70:30, resulting in 840 training data and 360 testing data. The best accuracy achieved by this model is 89% for training and 82% for validation. Therefore, it can be concluded that the model is able to classify mushrooms based on Genus using the CNN method
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