Efficient deep learning architecture for the classification of diseased plant leaves
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Efficient deep learning architecture for the classification of diseased plant leaves |
| 2. | Creator | Author's name, affiliation, country | Muniyandi Sadhasivam; Annamalai University; India |
| 2. | Creator | Author's name, affiliation, country | Manoharan Kalaiselvi Geetha; Annamalai University; India |
| 2. | Creator | Author's name, affiliation, country | James Gladson Maria Britto; Malla Reddy College of Engineering; India |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | Classification; Deep learning; Feature extraction; Pattern recognition; Plant leaf |
| 4. | Description | Abstract | The classification of plant leaf diseases via machine learning and deep learning algorithms has a great deal of potential for enhancing agricultural operations by allowing the early and accurate diagnosis of diseases. These systems can potentially develop into useful instruments for environmentally responsible farming and increased food safety as technological advancements continue. In this work, an efficient deep learning architecture has been developed to classify the diseased plant leaves. A ten-layer architecture is designed, which includes 5-convolutional layers using different numbers of filters (32, 64, 128, 256, and 512) and for dimension reduction, five max-pooling layers are used. The PlantVillage dataset which consists of more than 50,000 plant leaf samples is used to analyze the proposed architecture's performance. The performances are evaluated across different training and testing configurations and different dropout configurations. When compared to well-known transfer learning methods using visual geometric group (VGG16), AlexNet, and GoogleNet architectures, the proposed architecture obtains a higher level of performance with 98.18% classification accuracy. |
| 5. | Publisher | Organizing agency, location | Institute of Advanced Engineering and Science |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2024-01-01 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://ijeecs.iaescore.com/index.php/IJEECS/article/view/35349 |
| 10. | Identifier | Digital Object Identifier (DOI) | http://doi.org/10.11591/ijeecs.v33.i1.pp198-206 |
| 11. | Source | Title; vol., no. (year) | Indonesian Journal of Electrical Engineering and Computer Science; Vol 33, No 1: January 2024 |
| 12. | Language | English=en | en |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2023 Institute of Advanced Engineering and Science This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |