植物自动识别:相关算法综述

Noor Aini Mohd Roslan, Norizan Mat Diah, Z. Ibrahim, H. M. Hanum, Marina Ismail
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引用次数: 1

摘要

植物是最重要的元素之一,因为它们提供氧气,这是人类生存所必需的。植物识别应用已经得到了广泛的发展,这些应用可以帮助植物学家解决各种现实问题。本文综述了机器学习和深度学习算法在植物识别中的应用。本文综述了2007年至2020年间用于植物鉴定和识别研究的不同算法。讨论的主要算法是卷积神经网络(CNN)、支持向量机(SVM)、人工神经网络(ANN)和k近邻(KNN)。本文还比较了所选算法的性能,并根据研究成果提出了最佳技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Plant Recognition: A Survey of Relevant Algorithms
Plants are one of the most important elements since they provide oxygen, which is necessary for human survival. Plant recognition applications have been widely developed, and these applications can help botanists tackle various real-world problems. This paper reviews machine learning and deep learning algorithms discussed for plant recognition. Different algorithms used for plant identification and recognition research between the year 2007 until the year 2020 are reviewed. The main algorithms discussed are Convolutional Neural Network (CNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), and K-Nearest Neighbours (KNN). This paper also compares the performance between selected algorithms and proposes the best technique from the research outcomes.
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