@article {10.3844/jcssp.2023.1387.1397,
article_type = {journal},
title = {Dataset of Selected Medicinal Plant Species of the Genus Brachylaena: A Comparative Application of Deep Learning Models for Plant Leaf Recognition},
author = {Deyi, Avuya and Fadja, Arnaud Nguembang and Goosen, Eleonora Deborah and Noundou, Xavier Siwe and Atemkeng, Marcellin},
volume = {19},
number = {11},
year = {2023},
month = {Oct},
pages = {1387-1397},
doi = {10.3844/jcssp.2023.1387.1397},
url = {https://thescipub.com/abstract/jcssp.2023.1387.1397},
abstract = {Since several active pharmaceutical ingredients are sourced from medicinal plants, identifying and classifying these plants are generally a valuable and essential task during the drug manufacturing process. For many years, identifying and classifying those plants have been exclusively done by experts in the domain, such as botanists and herbarium curators. Recently, powerful computer vision technologies, using deep learning or deep artificial neural networks, have been developed for classifying or identifying objects using images. A convolutional neural network is a deep learning architecture that outperforms previous state-of-the-art approaches in image classification and object detection based on its efficient feature extraction of images. This study investigated several pre-trained convolutional neural networks for identifying and classifying leaves of three species of the genus Brachylaena. The three species considered were Brachylaena discolor, Brachylaena ilicifolia, and Brachylaena elliptica. All three species are used medicinally by people in South Africa. We trained and evaluated different deep convolutional neural networks from 1259 labeled images of those plant species (at least 400 for each species) split into training, evaluation, and test sets. The best model provided a 98.26% accuracy using cross-validation with a confidence interval of ±2.16%.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}