# Assessing deep convolutional neural network models and their comparative performance for automated medicinal plant identification from leaf images

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/assessing-deep-convolutional-neural-network-models-and-their-comparative/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Biplob Dey,Jannatul Ferdous,Romel Ahmed,Juel Hossain |
| Citations | 70 |
| DOI | 10.1016/j.heliyon.2023.e23655 |
| Fields | Agricultural and Biological Sciences,Chemistry,Environmental Science |
| Open Access | true |
| OA Status | gold |
| OA URL | https://doi.org/10.1016/j.heliyon.2023.e23655 |
| OpenAlex ID | https://openalex.org/W4389670887 |
| PMID | 38187334 |
| Type | article |
| Year | 2023 |

## Paper authors

- [Biplob Dey](https://scholariq.org/researchers/biplob-dey/)
- [Romel Ahmed](https://scholariq.org/researchers/romel-ahmed/)

## Paper journal

- [Heliyon](https://scholariq.org/journals/heliyon/)

## Paper primary topic

- [Smart Agriculture and AI](https://scholariq.org/topics/smart-agriculture-and-ai/)

## Paper topics

- [Smart Agriculture and AI](https://scholariq.org/topics/smart-agriculture-and-ai/)
- [Spectroscopy and Chemometric Analyses](https://scholariq.org/topics/spectroscopy-and-chemometric-analyses/)
- [Remote Sensing in Agriculture](https://scholariq.org/topics/remote-sensing-in-agriculture/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.