# Web-SpikeSegNet: Deep Learning Framework for Recognition and Counting of Spikes From Visual Images of Wheat Plants

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/web-spikesegnet-deep-learning-framework-for-recognition-and-counting-of-spikes/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Tanuj Misra,Alka Arora,Sudeep Marwaha,Ranjeet Ranjan Jha,Mrinmoy Ray,Rajni Jain,A. R. Rao,Eldho Varghese,Shailendra Kumar,Sudhir Kumar,Aditya Nigam,Rabi Narayan Sahoo,Viswanathan Chinnusamy |
| Citations | 36 |
| DOI | 10.1109/access.2021.3080836 |
| Fields | Agricultural and Biological Sciences,Chemistry,Environmental Science |
| Open Access | true |
| OA Status | gold |
| OA URL | https://ieeexplore.ieee.org/ielx7/6287639/9312710/09432817.pdf |
| OpenAlex ID | https://openalex.org/W3129635925 |
| Type | article |
| Year | 2021 |

## Paper authors

- [Alka Arora](https://scholariq.org/researchers/alka-arora/)

## Paper journal

- [IEEE Access](https://scholariq.org/journals/ieee-access/)

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

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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.