Alessia Cogato

891 total citations · 1 hit paper
26 papers, 582 citations indexed

About

Alessia Cogato is a scholar working on Plant Science, Ecology and Global and Planetary Change. According to data from OpenAlex, Alessia Cogato has authored 26 papers receiving a total of 582 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Plant Science, 15 papers in Ecology and 8 papers in Global and Planetary Change. Recurrent topics in Alessia Cogato's work include Horticultural and Viticultural Research (17 papers), Remote Sensing in Agriculture (14 papers) and Plant Water Relations and Carbon Dynamics (5 papers). Alessia Cogato is often cited by papers focused on Horticultural and Viticultural Research (17 papers), Remote Sensing in Agriculture (14 papers) and Plant Water Relations and Carbon Dynamics (5 papers). Alessia Cogato collaborates with scholars based in Italy, Australia and United Kingdom. Alessia Cogato's co-authors include Francesco Marinello, Marco Sozzi, Franco Meggio, Ahmed Kayad, Massimiliano De Antoni Migliorati, Andrea Pezzuolo, Vinay Pagay, Hao Guo, Cassandra Collins and Marta Brščić and has published in prestigious journals such as SHILAP Revista de lepidopterología, Sustainability and Remote Sensing.

In The Last Decade

Alessia Cogato

24 papers receiving 568 citations

Hit Papers

Automatic Bunch Detection... 2022 2026 2023 2024 2022 50 100 150

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Alessia Cogato Italy 11 351 192 98 87 61 26 582
Marco Sozzi Italy 14 520 1.5× 353 1.8× 119 1.2× 159 1.8× 53 0.9× 50 836
Benoît Mercatoris Belgium 19 374 1.1× 227 1.2× 62 0.6× 143 1.6× 58 1.0× 50 973
Shengping Liu China 10 252 0.7× 235 1.2× 70 0.7× 98 1.1× 20 0.3× 41 563
Ajay Sharda United States 16 509 1.5× 189 1.0× 65 0.7× 92 1.1× 42 0.7× 75 816
Muhammad Naveed Tahir Pakistan 15 303 0.9× 150 0.8× 120 1.2× 82 0.9× 23 0.4× 45 613
Sergio Vélez Netherlands 14 303 0.9× 245 1.3× 65 0.7× 164 1.9× 46 0.8× 42 493
Tiecheng Bai China 15 195 0.6× 174 0.9× 108 1.1× 109 1.3× 32 0.5× 45 526
Louis Longchamps United States 14 312 0.9× 168 0.9× 60 0.6× 126 1.4× 27 0.4× 36 534
H.J. van de Zedde Netherlands 9 502 1.4× 157 0.8× 35 0.4× 97 1.1× 42 0.7× 13 693
Ashley Wheaton Australia 8 456 1.3× 196 1.0× 178 1.8× 82 0.9× 49 0.8× 9 617

Countries citing papers authored by Alessia Cogato

Since Specialization
Citations

This map shows the geographic impact of Alessia Cogato'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 Alessia Cogato with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alessia Cogato more than expected).

Fields of papers citing papers by Alessia Cogato

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alessia Cogato. 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 Alessia Cogato. The network helps show where Alessia Cogato may publish in the future.

Co-authorship network of co-authors of Alessia Cogato

This figure shows the co-authorship network connecting the top 25 collaborators of Alessia Cogato. A scholar is included among the top collaborators of Alessia Cogato 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 Alessia Cogato. Alessia Cogato is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Singh, Ajit, Lawal Billa, Debbie L. Sparkes, et al.. (2024). Can Multi-Temporal Vegetation Indices and Machine Learning Algorithms Be Used for Estimation of Groundnut Canopy State Variables?. Horticulturae. 10(7). 748–748. 1 indexed citations
2.
Pezzuolo, Andrea, et al.. (2024). Applications of satellite platforms and machine learning for mapping and monitoring grasslands and pastures: A systematic and comprehensive review. SHILAP Revista de lepidopterología. 9. 100571–100571. 7 indexed citations
3.
Gautam, Deepak, Debbie L. Sparkes, Ajit Singh, et al.. (2024). Integrating Hyperspectral, Thermal, and Ground Data with Machine Learning Algorithms Enhances the Prediction of Grapevine Yield and Berry Composition. Remote Sensing. 16(23). 4539–4539. 1 indexed citations
4.
Cogato, Alessia, et al.. (2024). Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment. AgriEngineering. 6(4). 4107–4134.
5.
Cogato, Alessia, Leonardo Cei, Francesco Marinello, & Andrea Pezzuolo. (2023). The Role of Buildings in Rural Areas: Trends, Challenges, and Innovations for Sustainable Development. Agronomy. 13(8). 1961–1961. 6 indexed citations
6.
Cogato, Alessia, Francesco Marinello, & Andrea Pezzuolo. (2023). Soil Footprint and Land-Use Change to Clean Energy Production: Implications for Solar and Wind Power Plants. Land. 12(10). 1822–1822. 3 indexed citations
8.
Sozzi, Marco, et al.. (2022). Automatic Bunch Detection in White Grape Varieties Using YOLOv3, YOLOv4, and YOLOv5 Deep Learning Algorithms. Agronomy. 12(2). 319–319. 167 indexed citations breakdown →
9.
Sozzi, Marco, et al.. (2022). wGrapeUNIPD-DL: An open dataset for white grape bunch detection. Data in Brief. 43. 108466–108466. 12 indexed citations
10.
Cogato, Alessia, et al.. (2022). Evaluation of the physiological and spectral responses of grapevines (Vitis vinifera L.) under different durations of drought stress under high temperature conditions. Research Padua Archive (University of Padua). 368–372. 1 indexed citations
11.
Cogato, Alessia, Francesco Marinello, Franco Meggio, et al.. (2022). Water Stress Impacts on Grapevines (Vitis vinifera L.) in Hot Environments: Physiological and Spectral Responses. Agronomy. 12(8). 1819–1819. 14 indexed citations
12.
13.
Sozzi, Marco, et al.. (2021). 23. Validation of a commercial optoelectronics device for grape quality analysis. Padua Research Archive (University of Padova). 199–205. 2 indexed citations
14.
Sozzi, Marco, et al.. (2021). 22. Grape yield spatial variability assessment using YOLOv4 object detection algorithm. Padua Research Archive (University of Padova). 193–198. 10 indexed citations
15.
Cogato, Alessia, Franco Meggio, Cassandra Collins, & Francesco Marinello. (2020). Medium-Resolution Multispectral Data from Sentinel-2 to Assess the Damage and the Recovery Time of Late Frost on Vineyards. Remote Sensing. 12(11). 1896–1896. 32 indexed citations
16.
Cogato, Alessia, Andrea Pezzuolo, Claus Aage Grøn Sørensen, et al.. (2020). A GIS-Based Multicriteria Index to Evaluate the Mechanisability Potential of Italian Vineyard Area. Land. 9(11). 469–469. 21 indexed citations
17.
Sozzi, Marco, et al.. (2020). On-the-go variable rate fertilizer application on vineyard using a proximal spectral sensor. Research Padua Archive (University of Padua). 343–347. 12 indexed citations
18.
Cogato, Alessia, Andrea Pezzuolo, Marco Sozzi, & Francesco Marinello. (2020). A sample of Italian vineyards: Landscape and management parameters dataset. SHILAP Revista de lepidopterología. 33. 106589–106589. 7 indexed citations
19.
Cogato, Alessia, Franco Meggio, Massimiliano De Antoni Migliorati, & Francesco Marinello. (2019). Extreme Weather Events in Agriculture: A Systematic Review. Sustainability. 11(9). 2547–2547. 127 indexed citations
20.
Cogato, Alessia, et al.. (2019). Analysis and impact of recent climate trends on grape composition in north-east Italy. SHILAP Revista de lepidopterología. 13. 4014–4014. 10 indexed citations

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.

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