利用无人机图像实现CNN植物识别

IF 0.7 Q3 COMPUTER SCIENCE, THEORY & METHODS
M. A. Haq, Ahsan Ahmed, J. Gyani
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引用次数: 1

摘要

植物是世界上最重要的资源,因为它们是氧气的唯一天然来源。此外,植物被认为是至关重要的,因为它们是人类能量的主要来源,具有营养、治疗和其他益处。在这个技术驱动的世界里,图像识别变得更加突出,在这个领域正在发生许多创新。研究人员越来越多地使用图像处理技术来识别植物。随着时间的推移,卷积神经网络(CNN)将从庞大的标准数据集学习到的权重转移到具有较小集合或更特定数据的任务的能力已经得到了改善。使用深度学习和机器学习(ML)算法进行图像识别的几个应用程序。植物图像识别就是其中的一个突出部分。利用手机和相机采集的自然场景不同地点、9种不同植物的约300幅植物图像数据集进行训练。将五层卷积神经网络(CNN)应用于自然环境下的大规模植物分类。基于实验数据的植物识别具有较高的准确性。该模型在NU108数据集上达到了96%的最高识别率,NU101无人机图像的准确率达到了97.8%。关键词:卷积神经网络(CNN);机器学习(ML)算法;植物图像识别;植物图像数据集
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of CNN for Plant Identification using UAV Imagery
Plants are the world's most significant resource since they are the only natural source of oxygen. Additionally, plants are considered crucial since they are the major source of energy for humanity and have nutritional, therapeutic, and other benefits. Image identification has become more prominent in this technology-driven world, where many innovations are happening in this sphere. Image processing techniques are increasingly being used by researchers to identify plants. The capacity of Convolutional Neural Networks (CNN) to transfer weights learned with huge standard datasets to tasks with smaller collections or more particular data has improved over time. Several applications are made for image identification using deep learning, and Machine Learning (ML) algorithms. Plant image identification is a prominent part of such. The plant image dataset of about 300 images collected by mobile phone and camera from different places in the natural scenes with nine species of different plants are deployed for training. A fivelayered convolution neural network (CNN) is applied for largescale plant classification in a natural environment. The proposed work claims a higher accuracy in plant identification based on experimental data. The model achieves the utmost recognition rate of 96% NU108 dataset and UAV images of NU101 have achieved an accuracy of 97.8%. Keywords—Convolutional Neural Networks (CNN); Machine Learning (ML) algorithms; plant image identification; plant image dataset
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来源期刊
CiteScore
2.30
自引率
22.20%
发文量
519
期刊介绍: IJACSA is a scholarly computer science journal representing the best in research. Its mission is to provide an outlet for quality research to be publicised and published to a global audience. The journal aims to publish papers selected through rigorous double-blind peer review to ensure originality, timeliness, relevance, and readability. In sync with the Journal''s vision "to be a respected publication that publishes peer reviewed research articles, as well as review and survey papers contributed by International community of Authors", we have drawn reviewers and editors from Institutions and Universities across the globe. A double blind peer review process is conducted to ensure that we retain high standards. At IJACSA, we stand strong because we know that global challenges make way for new innovations, new ways and new talent. International Journal of Advanced Computer Science and Applications publishes carefully refereed research, review and survey papers which offer a significant contribution to the computer science literature, and which are of interest to a wide audience. Coverage extends to all main-stream branches of computer science and related applications
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