# Tomato leaf segmentation algorithms for mobile phone applications using deep learning

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/tomato-leaf-segmentation-algorithms-for-mobile-phone-applications-using-deep/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Lawrence C. Ngugi,Moataz M. Abdelwahab,Mohammed Abo‐Zahhad |
| Citations | 145 |
| DOI | 10.1016/j.compag.2020.105788 |
| Fields | Agricultural and Biological Sciences,Environmental Science |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W3087173802 |
| Type | article |
| Year | 2020 |

## Paper authors

- [Lawrence C. Ngugi](https://scholariq.org/researchers/lawrence-c-ngugi/)
- [Moataz M. Abdelwahab](https://scholariq.org/researchers/moataz-m-abdelwahab/)
- [Mohammed Abo‐Zahhad](https://scholariq.org/researchers/mohammed-abo-zahhad/)

## Paper journal

- [Computers and Electronics in Agriculture](https://scholariq.org/journals/computers-and-electronics-in-agriculture/)

## 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/)
- [Leaf Properties and Growth Measurement](https://scholariq.org/topics/leaf-properties-and-growth-measurement/)
- [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.