基于卷积神经网络的植物叶片识别

Aakash Ram S, Chrisline Sam C, Bennet Niffin N
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引用次数: 0

摘要

植物对人类很有用,可以提供食物、药物、燃料、纤维、住所等。但重要的是要确定一种植物的类型和用途,以利用它的好处。因此,我们提出了一种自动深度学习算法,利用叶子对植物进行适当的分类。在自然环境中捕获不同的植物图像,并创建包含12798张白色背景叶子图像的Leaf数据集。采用预处理、分割和模式匹配技术,得到了期望的输出。使用卷积神经网络作为模式匹配器来比较输入图像与数据集中图像的信息。因此,我们使用卷积神经网络算法获得了95%到99%的准确率。因此,本文将为植物学家、工业家、食品工程师、医生等提供利用叶片识别植物的有用信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of plants using leaf based on convolutional neural network
Plants are useful to humans by providing food, medicine, fuel, fibre, shelter etc. But it is important to identify the type and uses of a plant to utilize its benefits. So, we have proposed an automated deep learning algorithm to classify plants into appropriate taxonomy using a leaf. Different plant images are captured in a natural environment and created a Leaf dataset containing 12798 leaf images with white background. Preprocessing, segmentation and pattern matching techniques were used to obtain the desired output. Convolutional Neural Network has been used as a pattern matcher to compare the information of an input image with the images in the dataset. Thus, we obtained an accuracy ranging from 95% to 99% by using Convolutional Neural Network algorithm. Hence, this paper will be useful to identify plants using a leaf for Botanists, Industrialists, Food Engineers, Physicians, etc.
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