LeafsnapNet: An Experimentally Evolved Deep Learning Model for Recognition of Plant Species based on Leafsnap Image Dataset
| dc.contributor.author | Adetiba, Emmanuel | |
| dc.contributor.author | Ajayi, Oluwaseun T. | |
| dc.contributor.author | Kala, Jules R. | |
| dc.date.accessioned | 2023-08-22T14:43:00Z | |
| dc.date.available | 2023-08-22T14:43:00Z | |
| dc.date.issued | 2021-01-07 | |
| dc.description.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 | en_US |
| dc.description.sponsorship | ACE: Applied Informatics and Communication | en_US |
| dc.identifier.issn | 1549-3636 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/2071 | |
| dc.language.iso | en | en_US |
| dc.publisher | Journal of Computer Science | en_US |
| dc.relation.ispartofseries | Journal of Computer Science;2021, 17 (3) | |
| dc.subject | CNN | en_US |
| dc.subject | Leafsnap | en_US |
| dc.subject | MobileNetV2 | en_US |
| dc.subject | Optimizer | en_US |
| dc.subject | Plant Species | en_US |
| dc.subject | ACE: Applied Informatics and Communication | en_US |
| dc.subject | CAPiC | en_US |
| dc.subject | Covenant University | en_US |
| dc.subject | Digital Development | en_US |
| dc.subject | Nigeria | en_US |
| dc.subject | Joke A. Badejo | en_US |
| dc.subject | Sunday Ajala | en_US |
| dc.subject | Abdultaofeek Abayomi | en_US |
| dc.title | LeafsnapNet: An Experimentally Evolved Deep Learning Model for Recognition of Plant Species based on Leafsnap Image Dataset | en_US |
| dc.type | Article | en_US |