Aitor Álvarez-Gila
- Plant Science top 2%
- Computer Vision and Pattern Recognition top 1%
- Analytical Chemistry top 1%
- Media Technology top 1%
- Ecology top 10%
- Co-authors
- Artzai PicónAdrián GaldránDavid PardoJone EchazarraAmaia Ortiz‐BarredoUnai IrustaSergio Rodríguez-VaamondeAna María Díez-Navajas
- Topics
- Smart Agriculture and AI (4 papers)Plant Disease Management Techniques (3 papers)Advanced Image Processing Techniques (2 papers)
In The Last Decade
Aitor Álvarez-Gila
10 papers receiving 1.8k citations
Hit Papers
Peers
Comparison fields: 5 of 100
- Plant Science 981
- Computer Vision and Pattern Recognition 750
- Analytical Chemistry 391
- Media Technology 365
- Ecology 211
Countries citing papers authored by Aitor Álvarez-Gila
This map shows the geographic impact of Aitor Álvarez-Gila's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Aitor Álvarez-Gila with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aitor Álvarez-Gila more than expected).
Fields of papers citing papers by Aitor Álvarez-Gila
This network shows the impact of papers produced by Aitor Álvarez-Gila. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Aitor Álvarez-Gila. The network helps show where Aitor Álvarez-Gila may publish in the future.
Co-authorship network of co-authors of Aitor Álvarez-Gila
This figure shows the co-authorship network connecting the top 25 collaborators of Aitor Álvarez-Gila. A scholar is included among the top collaborators of Aitor Álvarez-Gila based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Aitor Álvarez-Gila. Aitor Álvarez-Gila is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 6 | |
| 4 | 20 | |
| 5 | Few-Shot Learning approach for plant disease classification using images taken in the fieldbreakdown → | 248 |
| 6 | 57 | |
| 7 | 5 | |
| 8 | 152 | |
| 9 | Deep convolutional neural networks for mobile capture device-based crop disease classification in the wildbreakdown → | 367 |
| 10 | Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use casebreakdown → | 300 |
| 11 | Automatic Red-Channel underwater image restorationbreakdown → | 725 |
About Aitor Álvarez-Gila
Aitor Álvarez-Gila is a scholar working on Media Technology, Biophysics and Radiation, having authored 11 papers that have together received 1.9k indexed citations. Recurring topics across this work include Smart Agriculture and AI (4 papers), Plant Disease Management Techniques (3 papers) and Advanced Image Processing Techniques (2 papers). The work is most often cited by research in Media Technology (365 citations), Analytical Chemistry (391 citations) and Computer Vision and Pattern Recognition (750 citations). Aitor Álvarez-Gila has collaborated with scholars based in Spain, Germany and France. Frequent co-authors include Artzai Picón, Adrián Galdrán, David Pardo, Jone Echazarra, Amaia Ortiz‐Barredo, Unai Irusta, Sergio Rodríguez-Vaamonde, Ana María Díez-Navajas, Alfonso Medela and Arantza Bereciartúa-Pérez. Their work appears in journals such as Physical Review Letters, PLoS ONE and Electrochimica Acta.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.