# Huiru Zhou

**Type:** Researchers  
**Canonical URL:** https://scholariq.org/researchers/huiru-zhou/

## Facts

| Field | Value |
| --- | --- |
| Citations | 83 |
| Field | Smart Agriculture and AI |
| h-index | 4 |
| i10-index | 3 |
| Last Known Institution | South China Agricultural University |
| OpenAlex ID | https://openalex.org/A5030347313 |
| ORCID iD | 0009-0002-9348-5117 |
| Works | 12 |

## Researcher papers

- [Applying convolutional neural networks for detecting wheat stripe rust transmission centers under complex field conditions using RGB-based high spatial resolution images from UAVs](https://scholariq.org/papers/applying-convolutional-neural-networks-for-detecting-wheat-stripe-rust/)
- [Automatic Detection of Rice Blast Fungus Spores by Deep Learning-Based Object Detection: Models, Benchmarks and Quantitative Analysis](https://scholariq.org/papers/automatic-detection-of-rice-blast-fungus-spores-by-deep-learning-based-object/)
- [Effects of Image Dataset Configuration on the Accuracy of Rice Disease Recognition Based on Convolution Neural Network](https://scholariq.org/papers/effects-of-image-dataset-configuration-on-the-accuracy-of-rice-disease/)
- [Recognition of multi-symptomatic rice leaf blast in dual scenarios by using convolutional neural networks](https://scholariq.org/papers/recognition-of-multi-symptomatic-rice-leaf-blast-in-dual-scenarios-by-using/)
- [Deep recognition of rice disease images: how many training samples do we really need?](https://scholariq.org/papers/deep-recognition-of-rice-disease-images-how-many-training-samples-do-we-really/)
- [Deep Contrastive Incomplete Time Series Clustering](https://scholariq.org/papers/deep-contrastive-incomplete-time-series-clustering/)
- [DeCITS: Incomplete Time Series Clustering with Deep Contrastive Learning](https://scholariq.org/papers/decits-incomplete-time-series-clustering-with-deep-contrastive-learning/)
- [YOLO-RBSD: an efficient and accurate rice blast spore detector based on improved YOLOv8](https://scholariq.org/papers/yolo-rbsd-an-efficient-and-accurate-rice-blast-spore-detector-based-on-improved/)
- [YOLO-RBSD: an efficient and accurate rice blast spore detector based on improved YOLOv8](https://scholariq.org/papers/yolo-rbsd-an-efficient-and-accurate-rice-blast-spore-detector-based-on-improved-2/)
- [YOLO-RBSD: an efficient and accurate rice blast spore detector based on improved YOLOv8](https://scholariq.org/papers/yolo-rbsd-an-efficient-and-accurate-rice-blast-spore-detector-based-on-improved-3/)

## Researcher 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/)
- [Mycotoxins in Agriculture and Food](https://scholariq.org/topics/mycotoxins-in-agriculture-and-food/)
- [Indoor Air Quality and Microbial Exposure](https://scholariq.org/topics/indoor-air-quality-and-microbial-exposure/)
- [Bacillus and Francisella bacterial research](https://scholariq.org/topics/bacillus-and-francisella-bacterial-research/)

## Researcher university

- [South China Agricultural University](https://scholariq.org/institutions/south-china-agricultural-university/)

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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.