LeafsnapNet: An Experimentally Evolved Deep Learning Model for Recognition of Plant Species based on Leafsnap Image Dataset
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Date
2021-01-07
Journal Title
Journal ISSN
Volume Title
Publisher
Journal of Computer Science
Abstract
Plants are very important living organisms on earth because
humans and animals depend on them for nutrition, oxygen, medicine and
balance in the ecosystem. Therefore, plant species recognition is critical to
the improvement of agricultural productivity, mitigation of climate change
and the discovery of new medicinal plants. However, species recognition
has remained a difficult task even for trained botanists, because using the
traditional approaches, an expert on a specie may be unfamiliar with others.
Thus, researchers and practitioners are increasingly interested in the
automation of species recognition problem. Recently, deep learning
algorithms such as Convolutional Neural Network (CNN) have provided
huge breakthroughs in various computer vision tasks compared to their
shallow predecessors. Deep learning automates features extraction by
learning salient representations of the data and subsequently classifies the
features using a supervised learning approach. Inspired by this capability,
we leveraged on five pre-trained CNN models and Leafsnap image dataset
of 185 plant species to experimentally evolve an accurate species
recognition model in this study. Among the pre-trained models,
MobileNetV2 with ADAM optimizer gave the highest testing accuracy of
92.33%. This result provides a basis for developing a mobile app for
automated species recognition on the field. This will augment existing
efforts to alleviate the difficulties of manual species recognition by
botanists, farmers, biologists, nature tourists as well as conservationists
Description
Keywords
CNN, Leafsnap, MobileNetV2, Optimizer, Plant Species, ACE: Applied Informatics and Communication, CAPiC, Covenant University, Digital Development, Nigeria, Joke A. Badejo, Sunday Ajala, Abdultaofeek Abayomi