Nicola Landro
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- Advanced Neural Network Applications 4
- Advanced Vision and Imaging 2
- Media Technology top 10%
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- Remote Sensing in Agriculture 2
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- Smart Agriculture and AI 3
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- Anomaly Detection Techniques and Applications 4
- Domain Adaptation and Few-Shot Learning 3
- Adversarial Robustness in Machine Learning 2
- Machine Learning and Data Classification 2
- Co-authors
- Ignazio GalloRiccardo La GrassaMirco BoschettiAnwar Ur RehmanLuigi RanghettiShah NawazCristina ReG. Cremonese
- Journals
- Remote Sensing (2 papers)ISPRS International Journal of Geo-Information (1 paper)Sustainability (1 paper)
- Partner nations
- Italy
In The Last Decade
Nicola Landro
16 papers receiving 256 citations
Hit Papers
Peers
Comparison fields: 5 of 75
- Computer Vision and Pattern Recognition 67
- Media Technology 27
- Ecology 64
- Plant Science 91
- Analytical Chemistry 22
Countries citing papers authored by Nicola Landro
This map shows the geographic impact of Nicola Landro'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 Nicola Landro with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicola Landro more than expected).
Fields of papers citing papers by Nicola Landro
This network shows the impact of papers produced by Nicola Landro. 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 Nicola Landro. The network helps show where Nicola Landro may publish in the future.
Co-authorship network
The 10 scholars most cited alongside Nicola Landro, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 1 | |
| 2 | Deep Object Detection of Crop Weeds: Performance of YOLOv7 on a Real Case Dataset from UAV Imagesbreakdown → | 2023 | 131 |
| 3 | 2022 | 4 | |
| 4 | 2022 | 13 | |
| 5 | 2022 | 30 | |
| 6 | 2022 | 7 | |
| 7 | 2022 | 5 | |
| 8 | 2021 | 10 | |
| 9 | 2021 | 10 | |
| 10 | 2021 | 12 | |
| 11 | 2021 | 3 | |
| 12 | 2021 | 1 | |
| 13 | 2020 | 19 | |
| 14 | 2020 | 2 | |
| 15 | 2020 | 4 | |
| 16 | 2019 | 10 |
About Nicola Landro
Nicola Landro is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology, having authored 16 papers that have together received 262 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (4 papers), Anomaly Detection Techniques and Applications (4 papers), Smart Agriculture and AI (3 papers), Domain Adaptation and Few-Shot Learning (3 papers), Remote Sensing in Agriculture (2 papers), Adversarial Robustness in Machine Learning (2 papers), Machine Learning and Data Classification (2 papers) and Advanced Vision and Imaging (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (67 citations), Media Technology (27 citations) and Ecology (64 citations). Nicola Landro has collaborated with scholars based in Italy. Frequent co-authors include Ignazio Gallo, Riccardo La Grassa, Mirco Boschetti, Anwar Ur Rehman, Luigi Ranghetti, Shah Nawaz, Cristina Re, G. Cremonese, Claudio Pernechele and Emanuele Simioni. Their work appears in journals such as Remote Sensing, ISPRS International Journal of Geo-Information, Sustainability, ISPRS Journal of Photogrammetry and Remote Sensing and Neurocomputing.
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.