# RustQNet: Multimodal deep learning for quantitative inversion of wheat stripe rust disease index

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/rustqnet-multimodal-deep-learning-for-quantitative-inversion-of-wheat-stripe/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Jie Deng,Danfeng Hong,Chengrun Li,Jing Yao,Ziqian Yang,Zhijian Zhang,Jocelyn Chanussot |
| Citations | 57 |
| DOI | 10.1016/j.compag.2024.109245 |
| Fields | Agricultural and Biological Sciences,Biochemistry, Genetics and Molecular Biology |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W4401030973 |
| Type | article |
| Year | 2024 |

## Paper authors

- [Jie Deng](https://scholariq.org/researchers/jie-deng-2/)

## Paper journal

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

## Paper primary topic

- [Wheat and Barley Genetics and Pathology](https://scholariq.org/topics/wheat-and-barley-genetics-and-pathology/)

## Paper topics

- [Wheat and Barley Genetics and Pathology](https://scholariq.org/topics/wheat-and-barley-genetics-and-pathology/)
- [Genetic Mapping and Diversity in Plants and Animals](https://scholariq.org/topics/genetic-mapping-and-diversity-in-plants-and-animals/)
- [Smart Agriculture and AI](https://scholariq.org/topics/smart-agriculture-and-ai/)

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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.