Insect Identification Among Deep Learning’s Meta-architectures Using Tensor Flow
Deven J. Patel1, Nirav Bhatt2
1Deven J. Patel*, Research Scholar, School of Engineering, RK University, Rajkot & Assistant Professor, Information Technology Cell, Junagadh Agricultural University, Junagadh, Gujarat, India.
2Nirav Bhatt, Associate Professor, School of Engineering, RK University, Rajkot, Gujarat, India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 1910-1914 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1031109119/2019©BEIESP | DOI: 10.35940/ijeat.A1031.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Agriculture provides food for human existence, where insects damage the crops. The identification of the insect is a difficult process and subjected to expert opinion. In recent years, researches using deep learning in fields of object detection have been widespread and show accuracy as a result. This study show the comparison of three widely used deep learning meta-architectures (Faster R-CNN, SSD Inception and SSD Mobilenet) as object detection for selected flying insects namely Phyllophaga spp., Helicoverpa armigera and Spodoptera litura. The proposed study is focused on accuracy performance of selected meta-architectures using small dataset of insects. The meta-architecture was tested with same environment for all three architectures and Faster RCNN meta-architecture performs outstanding with an accuracy of 95.33%.
Keywords: CNN, Deep Learning, Insect Detection, Object Detection, Pest Classification, TensorFlow