Mario Valerio Giuffrida
- Plant Science top 10%
- Ecology top 10%
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Radiology, Nuclear Medicine and Imaging
- Co-authors
- Sotirios A. TsaftarisMassimo MinerviniT. A. JoyceAgisilaos ChartsiasPeter DoernerPierdomenico PerataBruno MartoranoHanno Scharr
- Topics
- Smart Agriculture and AI (11 papers)Remote Sensing in Agriculture (6 papers)Leaf Properties and Growth Measurement (4 papers)
- Partner nations
- United KingdomItalyBelgium
In The Last Decade
Mario Valerio Giuffrida
24 papers receiving 688 citations
Peers
Comparison fields: 5 of 112
- Plant Science 331
- Ecology 195
- Computer Vision and Pattern Recognition 163
- Artificial Intelligence 97
- Radiology, Nuclear Medicine and Imaging 72
Countries citing papers authored by Mario Valerio Giuffrida
This map shows the geographic impact of Mario Valerio Giuffrida'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 Mario Valerio Giuffrida with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mario Valerio Giuffrida more than expected).
Fields of papers citing papers by Mario Valerio Giuffrida
This network shows the impact of papers produced by Mario Valerio Giuffrida. 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 Mario Valerio Giuffrida. The network helps show where Mario Valerio Giuffrida may publish in the future.
Co-authorship network of co-authors of Mario Valerio Giuffrida
This figure shows the co-authorship network connecting the top 25 collaborators of Mario Valerio Giuffrida. A scholar is included among the top collaborators of Mario Valerio Giuffrida 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 Mario Valerio Giuffrida. Mario Valerio Giuffrida is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 3 | |
| 4 | 2 | |
| 5 | 0 | |
| 6 | 1 | |
| 7 | 2 | |
| 8 | 3 | |
| 9 | 4 | |
| 10 | 8 | |
| 11 | 35 | |
| 12 | 0 | |
| 13 | 45 | |
| 14 | 6 | |
| 15 | 24 | |
| 16 | 65 | |
| 17 | Root Gap Correction with a Deep Inpainting Model | 6 |
| 18 | 25 | |
| 19 | 81 | |
| 20 | 65 |
About Mario Valerio Giuffrida
Mario Valerio Giuffrida is a scholar working on Ecological Modeling, Modeling and Simulation and Plant Science, having authored 28 papers that have together received 708 indexed citations. Recurring topics across this work include Smart Agriculture and AI (11 papers), Remote Sensing in Agriculture (6 papers) and Leaf Properties and Growth Measurement (4 papers). The work is most often cited by research in Plant Science (331 citations), Computer Vision and Pattern Recognition (163 citations) and Ecology (195 citations). Mario Valerio Giuffrida has collaborated with scholars based in United Kingdom, Italy and Belgium. Frequent co-authors include Sotirios A. Tsaftaris, Massimo Minervini, T. A. Joyce, Agisilaos Chartsias, Peter Doerner, Pierdomenico Perata, Bruno Martorano, Hanno Scharr, Feng Chen and Hao Chen. Their work appears in journals such as The Plant Journal, IEEE Transactions on Image Processing and IEEE Access.
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.