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Proceedings Paper

Identification of the genus of stingless bee via faster R-CNN
Author(s): A. Nizam; W. Mohd-Isa; A. Ali
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Paper Abstract

This study presents an interesting approach to identifying the Genus of the Stingless bee aided by machine learning technology. The conventional way of identifying the Genus of the Stingless bee or “Lebah Kelulut” relied on the face-to-face meetings with local bee experts. This particular process is considered to be outdated and time consuming. Thus, the proposed solution incorporated the machine learning tool called the “TensorFlow Object Detection API”. This tool is provided by Google TensorFlow and uses the Faster Region-based Convolutional Neural Network (Faster R-CNN), which incorporates the Region Proposal Network to enhance the current network. The data set used for training and testing consisted of 400 images, which belong to two types of bee species namely, the Heterotrigona Erythrogasta and Heterotrigona Itama. The evaluation of the model produced an accuracy rate of 73.75% for an average computing time per image of 0.65 seconds.

Paper Details

Date Published: 22 March 2019
PDF: 6 pages
Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 1104943 (22 March 2019); doi: 10.1117/12.2521380
Show Author Affiliations
A. Nizam, Multimedia Univ. (Malaysia)
W. Mohd-Isa, Multimedia Univ. (Malaysia)
A. Ali, Multimedia Univ. (Malaysia)

Published in SPIE Proceedings Vol. 11049:
International Workshop on Advanced Image Technology (IWAIT) 2019
Qian Kemao; Kazuya Hayase; Phooi Yee Lau; Wen-Nung Lie; Yung-Lyul Lee; Sanun Srisuk; Lu Yu, Editor(s)

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