# Alvaro Fuentes

**Type:** Researchers  
**Canonical URL:** https://scholariq.org/researchers/alvaro-fuentes/

## Facts

| Field | Value |
| --- | --- |
| Citations | 3,216 |
| Field | Smart Agriculture and AI |
| h-index | 17 |
| i10-index | 26 |
| Last Known Institution | Jeonbuk National University |
| OpenAlex ID | https://openalex.org/A5027683909 |
| ORCID iD | 0000-0001-8847-1541 |
| Works | 74 |

## Researcher papers

- [A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition](https://scholariq.org/papers/a-robust-deep-learning-based-detector-for-real-time-tomato-plant-diseases-and/)
- [A Comprehensive Survey of Image Augmentation Techniques for Deep Learning](https://scholariq.org/papers/a-comprehensive-survey-of-image-augmentation-techniques-for-deep-learning/)
- [High-Performance Deep Neural Network-Based Tomato Plant Diseases and Pests Diagnosis System With Refinement Filter Bank](https://scholariq.org/papers/high-performance-deep-neural-network-based-tomato-plant-diseases-and-pests/)
- [Deep learning-based hierarchical cattle behavior recognition with spatio-temporal information](https://scholariq.org/papers/deep-learning-based-hierarchical-cattle-behavior-recognition-with-spatio/)
- [Style-Consistent Image Translation: A Novel Data Augmentation Paradigm to Improve Plant Disease Recognition](https://scholariq.org/papers/style-consistent-image-translation-a-novel-data-augmentation-paradigm-to-improve/)
- [Deep Learning-Based Phenotyping System With Glocal Description of Plant Anomalies and Symptoms](https://scholariq.org/papers/deep-learning-based-phenotyping-system-with-glocal-description-of-plant/)
- [Deep learning-based multi-cattle tracking in crowded livestock farming using video](https://scholariq.org/papers/deep-learning-based-multi-cattle-tracking-in-crowded-livestock-farming-using/)
- [Causal Inference with Multilevel Data: A Comparison of Different Propensity Score Weighting Approaches](https://scholariq.org/papers/causal-inference-with-multilevel-data-a-comparison-of-different-propensity-score/)
- [Embracing limited and imperfect training datasets: opportunities and challenges in plant disease recognition using deep learning](https://scholariq.org/papers/embracing-limited-and-imperfect-training-datasets-opportunities-and-challenges/)
- [Deep Learning-Based Techniques for Plant Diseases Recognition in Real-Field Scenarios](https://scholariq.org/papers/deep-learning-based-techniques-for-plant-diseases-recognition-in-real-field/)
- [Unsupervised image translation using adversarial networks for improved plant disease recognition](https://scholariq.org/papers/unsupervised-image-translation-using-adversarial-networks-for-improved-plant/)

## Researcher 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/)
- [Video Surveillance and Tracking Methods](https://scholariq.org/topics/video-surveillance-and-tracking-methods/)
- [Plant Virus Research Studies](https://scholariq.org/topics/plant-virus-research-studies/)
- [Animal Behavior and Welfare Studies](https://scholariq.org/topics/animal-behavior-and-welfare-studies/)

## Researcher university

- [Jeonbuk National University](https://scholariq.org/institutions/jeonbuk-national-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.