Identification of the genus of stingless bee via faster R-CNN


Nizam, A. and Mohd Isa, Wan Noorshahida and Ali, A. (2019) Identification of the genus of stingless bee via faster R-CNN. International Workshop on Advanced Image Technology (IWAIT) 2019, 11049. ISSN 0277-786X

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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 This particular process is considered to be outdated and time consuming.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. © (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Item Type: Article
Uncontrolled Keywords: Machine learning
Subjects: Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 09 Feb 2022 01:49
Last Modified: 09 Feb 2022 01:49


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