# Feasibility of Hybrid PSO-ANN Model for Identifying Soybean Diseases

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/feasibility-of-hybrid-pso-ann-model-for-identifying-soybean-diseases/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Miaomiao Ji,Peng Liu,Qiufeng Wu |
| Citations | 8 |
| DOI | 10.4018/ijcini.290328 |
| Fields | Agricultural and Biological Sciences,Chemistry |
| Open Access | true |
| OA Status | diamond |
| OA URL | https://www.igi-global.com/ViewTitle.aspx?TitleId=290328&isxn=9781799859857 |
| OpenAlex ID | https://openalex.org/W3214216209 |
| Type | article |
| Year | 2021 |

## Paper authors

- [Miaomiao Ji](https://scholariq.org/researchers/miaomiao-ji/)

## Paper journal

- [International Journal of Cognitive Informatics and Natural Intelligence](https://scholariq.org/journals/international-journal-of-cognitive-informatics-and-natural-intelligence/)

## 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/)
- [Plant Pathogenic Bacteria Studies](https://scholariq.org/topics/plant-pathogenic-bacteria-studies/)

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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.