IDENTIFYING IMAGES OF INVASIVE HYDRANGEA USING PRE-TRAINED DEEP CONVOLUTIONAL NEURAL NETWORKS
Authors:
Belal A. M. Ashqar, Al-Azhar University-Gaza, Palestine
Samy S. Abu-Naser, Al-Azhar University-Gaza, Palestine
Abstract:
Invasive species are threatening habitats of native species in many countries around the world. The current methods of monitoring them depend on expert knowledge. Trained scientists visit designated areas and take note of the species inhabiting them. Using such a highly qualified workforce is expensive, time inefficient and insufficient since humans cannot cover large areas when sampling. In this paper, machine learning based approach is presented for identifying images of invasive hydrangea (a beautiful invasive species original of Asia) with a dataset that contains approximately 3,800 images taken in a Brazilian national forest and in some of the pictures there is Hydrangea. A deep learning technique that extensively applied to image recognition was used. Our trained model achieved an accuracy of 99.71% on a held-out test set, demonstrating the feasibility of this approach.
Keywords:
Invasive Species, Classification, Deep Learning
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Citations:
APA:
Ashqar, B. A. M., & Abu-Naser, S. S. (2019). Identifying Images of Invasive Hydrangea using Pre-Trained Deep Convolutional Neural Networks. International Journal of Control and Automation (IJCA), ISSN: 2005-4297 (Print); 2207-6387 (Online), NADIA, 12(4), 15-28. doi: 10.33832/ijca.2019.12.4.02.
MLA:
Ashqar, Belal A. M., et al. “Identifying Images of Invasive Hydrangea using Pre-Trained Deep Convolutional Neural Networks.” International Journal of Control and Automation, ISSN: 2005-4297 (Print); 2207-6387 (Online), NADIA, vol. 12, no. 4, 2019, pp. 15-28. IJCA, http://article.nadiapub.com/IJCA/vol12_no4/2.html.
IEEE:
[1] B. A. M. Ashqar, and S. S. Abu-Naser, "Identifying Images of Invasive Hydrangea using Pre-Trained Deep Convolutional Neural Networks." International Journal of Control and Automation (IJCA), ISSN: 2005-4297 (Print); 2207-6387 (Online), NADIA, vol. 12, no. 4, pp. 15-28, Apr 2019.
