# Huarui Wu

**Type:** Researchers  
**Canonical URL:** https://scholariq.org/researchers/huarui-wu/

## Facts

| Field | Value |
| --- | --- |
| Citations | 1,536 |
| Field | Smart Agriculture and AI |
| h-index | 20 |
| i10-index | 37 |
| Last Known Institution | Ministry of Agriculture and Rural Affairs |
| OpenAlex ID | https://openalex.org/A5100637733 |
| ORCID iD | 0000-0001-5739-6876 |
| Works | 117 |

## Researcher papers

- [A cucumber leaf disease severity classification method based on the fusion of DeepLabV3+ and U-Net](https://scholariq.org/papers/a-cucumber-leaf-disease-severity-classification-method-based-on-the-fusion-of/)
- [Advanced agricultural disease image recognition technologies: A review](https://scholariq.org/papers/advanced-agricultural-disease-image-recognition-technologies-a-review/)
- [Crop disease identification and interpretation method based on multimodal deep learning](https://scholariq.org/papers/crop-disease-identification-and-interpretation-method-based-on-multimodal-deep/)
- [Dual-branch, efficient, channel attention-based crop disease identification](https://scholariq.org/papers/dual-branch-efficient-channel-attention-based-crop-disease-identification/)
- [Intelligent alerting for fruit-melon lesion image based on momentum deep learning](https://scholariq.org/papers/intelligent-alerting-for-fruit-melon-lesion-image-based-on-momentum-deep/)
- [EFDet: An efficient detection method for cucumber disease under natural complex environments](https://scholariq.org/papers/efdet-an-efficient-detection-method-for-cucumber-disease-under-natural-complex/)
- [Detection of powdery mildew on strawberry leaves based on DAC-YOLOv4 model](https://scholariq.org/papers/detection-of-powdery-mildew-on-strawberry-leaves-based-on-dac-yolov4-model/)
- [Few-shot vegetable disease recognition model based on image text collaborative representation learning](https://scholariq.org/papers/few-shot-vegetable-disease-recognition-model-based-on-image-text-collaborative/)
- [A vegetable disease recognition model for complex background based on region proposal and progressive learning](https://scholariq.org/papers/a-vegetable-disease-recognition-model-for-complex-background-based-on-region/)
- [Tomato Disease Classification and Identification Method Based on Multimodal Fusion Deep Learning](https://scholariq.org/papers/tomato-disease-classification-and-identification-method-based-on-multimodal/)

## Researcher topics

- [Smart Agriculture and AI](https://scholariq.org/topics/smart-agriculture-and-ai/)
- [Energy Efficient Wireless Sensor Networks](https://scholariq.org/topics/energy-efficient-wireless-sensor-networks/)
- [Energy Harvesting in Wireless Networks](https://scholariq.org/topics/energy-harvesting-in-wireless-networks/)
- [Plant Disease Management Techniques](https://scholariq.org/topics/plant-disease-management-techniques/)
- [Indoor and Outdoor Localization Technologies](https://scholariq.org/topics/indoor-and-outdoor-localization-technologies/)

## Researcher university

- [Ministry of Agriculture and Rural Affairs](https://scholariq.org/institutions/ministry-of-agriculture-and-rural-affairs/)

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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.