Francisco Pinto

3.5k total citations
37 papers, 1.6k citations indexed

About

Francisco Pinto is a scholar working on Ecology, Plant Science and Global and Planetary Change. According to data from OpenAlex, Francisco Pinto has authored 37 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Ecology, 20 papers in Plant Science and 15 papers in Global and Planetary Change. Recurrent topics in Francisco Pinto's work include Remote Sensing in Agriculture (22 papers), Plant Water Relations and Carbon Dynamics (8 papers) and Wheat and Barley Genetics and Pathology (6 papers). Francisco Pinto is often cited by papers focused on Remote Sensing in Agriculture (22 papers), Plant Water Relations and Carbon Dynamics (8 papers) and Wheat and Barley Genetics and Pathology (6 papers). Francisco Pinto collaborates with scholars based in Mexico, Germany and Italy. Francisco Pinto's co-authors include Uwe Rascher, Matthew Reynolds, Micol Rossini, Sergio Cogliati, Anke Schickling, Mark Müller‐Linow, Cinzia Panigada, Mohd Anwar Khan, M. Ashraf Bhat and Reyazul Rouf Mir and has published in prestigious journals such as Bioinformatics, PLoS ONE and Remote Sensing of Environment.

In The Last Decade

Francisco Pinto

36 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Francisco Pinto Mexico 19 1.0k 866 465 268 241 37 1.6k
David M. Deery Australia 14 1.0k 1.0× 605 0.7× 196 0.4× 228 0.9× 330 1.4× 22 1.4k
Nadia Shakoor United States 16 829 0.8× 592 0.7× 148 0.3× 233 0.9× 347 1.4× 36 1.4k
Xavier Sirault Australia 22 1.7k 1.6× 673 0.8× 319 0.7× 281 1.0× 429 1.8× 38 2.2k
Omar Vergara‐Díaz Spain 20 1.1k 1.1× 772 0.9× 198 0.4× 126 0.5× 329 1.4× 37 1.5k
Matthew M. Conley United States 15 970 0.9× 417 0.5× 410 0.9× 140 0.5× 123 0.5× 30 1.3k
Llorenç Cabrera‐Bosquet France 23 1.9k 1.8× 560 0.6× 427 0.9× 396 1.5× 139 0.6× 40 2.4k
Alexis Comar France 18 1.1k 1.1× 1.2k 1.4× 289 0.6× 102 0.4× 668 2.8× 25 1.7k
Bodo Mistele Germany 22 1.4k 1.3× 1.4k 1.6× 285 0.6× 87 0.3× 462 1.9× 31 1.9k
Roland Pieruschka Germany 21 1.5k 1.4× 331 0.4× 503 1.1× 149 0.6× 74 0.3× 45 1.9k
A. Hornero Spain 18 915 0.9× 1.2k 1.3× 406 0.9× 47 0.2× 474 2.0× 39 1.6k

Countries citing papers authored by Francisco Pinto

Since Specialization
Citations

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

Fields of papers citing papers by Francisco Pinto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Francisco Pinto

This figure shows the co-authorship network connecting the top 25 collaborators of Francisco Pinto. A scholar is included among the top collaborators of Francisco Pinto 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 Francisco Pinto. Francisco Pinto 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.
Victor, Brandon, Aiden Nibali, Saul Newman, et al.. (2024). High-Throughput Plot-Level Quantitative Phenotyping Using Convolutional Neural Networks on Very High-Resolution Satellite Images. Remote Sensing. 16(2). 282–282. 5 indexed citations
2.
Montesinos‐López, Abelardo, Francisco Pinto, David S. González-González, et al.. (2023). Multimodal deep learning methods enhance genomic prediction of wheat breeding. G3 Genes Genomes Genetics. 13(5). 17 indexed citations
3.
McAusland, Lorna, Liana G. Acevedo‐Siaca, R. Suzuky Pinto, et al.. (2023). Night‐time warming in the field reduces nocturnal stomatal conductance and grain yield but does not alter daytime physiological responses. New Phytologist. 239(5). 1622–1636. 15 indexed citations
4.
Molero, Gemma, Ryan Joynson, Francisco Pinto, et al.. (2023). Exotic alleles contribute to heat tolerance in wheat under field conditions. Communications Biology. 6(1). 21–21. 27 indexed citations
5.
Molero, Gemma, Ryan Joynson, Francisco Pinto, et al.. (2022). Exotic alleles contribute to heat tolerance in wheat under field conditions. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
6.
Pinto, Francisco, et al.. (2022). Prediction of Photosynthetic, Biophysical, and Biochemical Traits in Wheat Canopies to Reduce the Phenotyping Bottleneck. Frontiers in Plant Science. 13. 828451–828451. 10 indexed citations
7.
Molero, Gemma, et al.. (2021). Field-based remote sensing models predict radiation use efficiency in wheat. Journal of Experimental Botany. 72(10). 3756–3773. 21 indexed citations
8.
Mondal, Suchismita, José Crossa, Ravi P. Singh, et al.. (2020). Aerial high‐throughput phenotyping enables indirect selection for grain yield at the early generation, seed‐limited stages in breeding programs. Crop Science. 60(6). 3096–3114. 39 indexed citations
9.
Pinto, Francisco, Marco Celesti, Kelvin Acebron, et al.. (2020). Dynamics of sun‐induced chlorophyll fluorescence and reflectance to detect stress‐induced variations in canopy photosynthesis. Plant Cell & Environment. 43(7). 1637–1654. 27 indexed citations
10.
Reynolds, Matthew, Scott Chapman, Leonardo Crespo‐Herrera, et al.. (2020). Breeder friendly phenotyping. Plant Science. 295. 110396–110396. 162 indexed citations
11.
Mir, Reyazul Rouf, Matthew Reynolds, Francisco Pinto, Mohd Anwar Khan, & M. Ashraf Bhat. (2019). High-throughput phenotyping for crop improvement in the genomics era. Plant Science. 282. 60–72. 180 indexed citations
12.
Celesti, Marco, Christiaan van der Tol, Sergio Cogliati, et al.. (2018). Exploring the physiological information of Sun-induced chlorophyll fluorescence through radiative transfer model inversion. Remote Sensing of Environment. 215. 97–108. 49 indexed citations
13.
Pinto, Francisco, Alexander Damm, Anke Schickling, et al.. (2016). Sun‐induced chlorophyll fluorescence from high‐resolution imaging spectroscopy data to quantify spatio‐temporal patterns of photosynthetic function in crop canopies. Plant Cell & Environment. 39(7). 1500–1512. 95 indexed citations
14.
Müller‐Linow, Mark, Francisco Pinto, Hanno Scharr, & Uwe Rascher. (2015). The leaf angle distribution of natural plant populations: assessing the canopy with a novel software tool. Plant Methods. 11(1). 11–11. 83 indexed citations
15.
Matveeva, Maria, et al.. (2014). Remote Sensing of Sun-induced Fluorescence to Measure the Functional Regulation of Photosynthesis. 2014 AGU Fall Meeting. 2014. 1 indexed citations
16.
Colombo, Roberto, Luis Alonso, Marco Celesti, et al.. (2014). Remote sensing of sun-induced chlorophyll fluorescence at different scales. University of Twente Research Information. 8889. 1–4. 1 indexed citations
17.
Pinto, Francisco, Michael Mielewczik, Frank Liebisch, et al.. (2013). Non-Invasive Measurement of Frog Skin Reflectivity in High Spatial Resolution Using a Dual Hyperspectral Approach. PLoS ONE. 8(9). e73234–e73234. 12 indexed citations
18.
Rascher, Uwe, Luis Guanter, J. Moreno, et al.. (2013). Mapping sun-induced fluorescence using the high performance imaging spectrometer HyPlant: Understanding spatio-temporal variations in vegetation stress response and functional adaptatation of photosynthesis. 2 indexed citations
19.
Römer, Christoph, Mirwaes Wahabzada, Agim Ballvora, et al.. (2012). Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis. Functional Plant Biology. 39(11). 878–890. 119 indexed citations
20.
Pinto, Francisco, et al.. (1999). Guidance Parameter Determination Using Artificial Neural Network Classifier. 2 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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