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CNN-Driven Plant Species Recognition System
Subject area: Science,Engineering and Technology · Area of research: Deep Learning, Image Processing
Abstract
The need for innovative solutions to monitor and sustain plant species biodiversity is rising because global biodiversity declines rapidly. The traditional methods of identifying plants are frequently time-consuming and require botanists with expertise in these areas. The objective is to create a dependable, efficient, and scalable system for recognizing plant species using machine learning technology. The intent here is to construct a user-friendly tool leveraging complex machine learning techniques such as Convolutional Neural Networks (CNN), which allow scientists and the public to identify plant species correctly. The suggested approach is based on an extensive dataset of photos that depict many plant species at various phases of growth in addition to varying environmental circumstances. This assists in classification and feature extraction by CNNs that enables the model to learn specific features from these pictures and increase its extent of generalization across various plant species. This method could significantly increase plant identification's availability, speed, and accuracy, supporting conservation efforts and monitoring the world's biodiversity.
Keywords
Classification, Convolutional Neural Networks, Deep Learning, Feature Extraction, Generalization, Machine Learning.
References
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[2] G. Chandrababu, O. T. Lee, and K. S. Rekha, "Identification of Plant Species using Deep Learning".
[3] Q. B. Truong, "Plant species identification from leaf patterns using histogram of oriented gradients feature space and convolution neural networks.
[4] C. S. Pereira, R. Morais, and M. J. C. S. Reis, "Deep Learning Techniques for Grape Plant Species Identification in Natural Images".
[5] S. Kaur and P. Kaur, "Plant Species Identification based on Plant Leaf Using Computer Vision and Machine Learning Techniques".
[6] J. Wäldchen and P. Mäder, "Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review," Archives of Computational Methods in Engineering.
[7] J. S. Cope, D. Corney, J. Y. Clark, P. Remagnino, and P. Wilkin, "Plant species identification using digital morphometrics: A review.
[8] N. Kumar, P. N. Belhumeur, A. Biswas, D. W. Jacobs, W. J. Kress, I. C. Lopez, and J. V. B. Soares, "Leafsnap: A Computer Vision System for Automatic Plant Species Identification,”.
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How to cite this paper
@article{1706518,
author = {Suchetha N V, Arjun K, Panchami B S},
title = {CNN-Driven Plant Species Recognition System},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {5},
pages = {358-366},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1706518.pdf},
abstract = {The need for innovative solutions to monitor and sustain plant species biodiversity is rising because global biodiversity declines rapidly. The traditional methods of identifying plants are frequently time-consuming and require botanists with expertise in these areas. The objective is to create a dependable, efficient, and scalable system for recognizing plant species using machine learning technology. The intent here is to construct a user-friendly tool leveraging complex machine learning techniques such as Convolutional Neural Networks (CNN), which allow scientists and the public to identify plant species correctly. The suggested approach is based on an extensive dataset of photos that depict many plant species at various phases of growth in addition to varying environmental circumstances. This assists in classification and feature extraction by CNNs that enables the model to learn specific features from these pictures and increase its extent of generalization across various plant species. This method could significantly increase plant identification's availability, speed, and accuracy, supporting conservation efforts and monitoring the world's biodiversity.},
keywords = {Classification, Convolutional Neural Networks, Deep Learning, Feature Extraction, Generalization, Machine Learning.},
month = {November},
}