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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 2, 2022.
Abstract: Plants leaves recognition is an important scientific field that is concerned of recognizing leaves using image processing techniques. Several methods are presented using different algorithms to achieve the highest possible accuracy. This paper provides an analytical survey of various methods used in image processing for the recognition of plants through their leaves. These methods help in extracting useful information for botanists to utilize the medicinal properties of these leaves, or for any other agricultural and environmental purposes. We also provide insights and a complete review of different techniques used by researchers that consider different features and classifiers. These features and classifiers are studied in term of their capabilities in enhancing the accuracy ratios of the classification methods. Our analysis shows that both of the Support Victor Machines (SVM) and the Convolutional Neural Network (CNN) are positively dominant among other methods in term of accuracy.
Khaled Suwais, Khattab Alheeti and Duaa Al_Dosary, “A Review on Classification Methods for Plants Leaves Recognition” International Journal of Advanced Computer Science and Applications(IJACSA), 13(2), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130211
@article{Suwais2022,
title = {A Review on Classification Methods for Plants Leaves Recognition},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130211},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130211},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {2},
author = {Khaled Suwais and Khattab Alheeti and Duaa Al_Dosary}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.