Open AccessInsect Identification Among Deep Learning’s Meta-architectures Using TensorFlow
Author(s) -
Dhruv Patel,
Nirav Bhatt
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a1031.109119
Subject(s) - artificial intelligence , deep learning , computer science , object detection , identification (biology) , meta learning (computer science) , machine learning , spodoptera litura , pattern recognition (psychology) , biology , lepidoptera genitalia , ecology , engineering , systems engineering , task (project management)
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%.