Sergio Cubero

4.7k total citations · 2 hit papers
61 papers, 3.2k citations indexed

About

Sergio Cubero is a scholar working on Analytical Chemistry, Plant Science and Biomedical Engineering. According to data from OpenAlex, Sergio Cubero has authored 61 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Analytical Chemistry, 37 papers in Plant Science and 14 papers in Biomedical Engineering. Recurrent topics in Sergio Cubero's work include Spectroscopy and Chemometric Analyses (50 papers), Smart Agriculture and AI (17 papers) and Advanced Chemical Sensor Technologies (13 papers). Sergio Cubero is often cited by papers focused on Spectroscopy and Chemometric Analyses (50 papers), Smart Agriculture and AI (17 papers) and Advanced Chemical Sensor Technologies (13 papers). Sergio Cubero collaborates with scholars based in Spain, Colombia and Italy. Sergio Cubero's co-authors include J. Blasco, Nuria Aleixos, Juan Gómez‐Sanchís, Enrique Moltó, Pau Talens, D. Lorente, Victoria Cortés, Sandra Munera, Oscar Leonardo García-Navarrete and J.M. Prats-Montalbán and has published in prestigious journals such as Trends in Food Science & Technology, Journal of Food Engineering and Remote Sensing.

In The Last Decade

Sergio Cubero

60 papers receiving 3.1k citations

Hit Papers

Recent Advances and Applications of Hyperspectral Imaging... 2011 2026 2016 2021 2011 2025 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sergio Cubero Spain 31 2.2k 1.6k 748 500 362 61 3.2k
Nuria Aleixos Spain 35 2.7k 1.2× 2.0k 1.3× 882 1.2× 508 1.0× 395 1.1× 76 4.1k
Shuxiang Fan China 37 3.1k 1.4× 1.6k 1.0× 1.1k 1.5× 441 0.9× 822 2.3× 89 3.8k
Manuela Zude-Sasse Germany 37 1.8k 0.8× 1.8k 1.1× 557 0.7× 526 1.1× 315 0.9× 139 3.5k
Min Huang China 30 1.6k 0.7× 897 0.6× 626 0.8× 321 0.6× 446 1.2× 135 2.7k
Jun Sun China 36 2.1k 1.0× 991 0.6× 1.0k 1.4× 360 0.7× 533 1.5× 158 3.4k
Jitendra Paliwal Canada 37 2.4k 1.1× 1.8k 1.2× 906 1.2× 931 1.9× 622 1.7× 188 4.3k
Leiqing Pan China 33 1.7k 0.8× 1.2k 0.8× 1.1k 1.5× 833 1.7× 297 0.8× 184 3.5k
Qibing Zhu China 28 1.5k 0.7× 1000 0.6× 569 0.8× 306 0.6× 407 1.1× 89 2.4k
Chunjiang Zhao China 25 1.5k 0.7× 774 0.5× 561 0.8× 249 0.5× 331 0.9× 76 2.2k
Wenqian Huang China 40 3.5k 1.6× 1.8k 1.1× 1.2k 1.6× 576 1.2× 922 2.5× 133 4.5k

Countries citing papers authored by Sergio Cubero

Since Specialization
Citations

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

Fields of papers citing papers by Sergio Cubero

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sergio Cubero

This figure shows the co-authorship network connecting the top 25 collaborators of Sergio Cubero. A scholar is included among the top collaborators of Sergio Cubero 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 Sergio Cubero. Sergio Cubero 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
2.
Munera, Sandra, et al.. (2025). Artificial Neural Networks in Agriculture, the core of artificial intelligence: What, When, and Why. Computers and Electronics in Agriculture. 230. 109938–109938. 19 indexed citations breakdown →
3.
Giménez, María J., et al.. (2024). Non-destructive assessment of 'Fino' lemon quality through ripening using NIRS and chemometric analysis. Postharvest Biology and Technology. 212. 112870–112870. 15 indexed citations
4.
Munera, Sandra, Alejandro Rodríguez, Sergio Cubero, Nuria Aleixos, & J. Blasco. (2024). Automatic detection of pomegranate fruit affected by blackheart disease using X-ray imaging. LWT. 215. 117248–117248. 5 indexed citations
5.
Hernández, Carlos, Flavio Prieto, Lluı́s Palou, et al.. (2023). New model for the automatic detection of anthracnose in mango fruits based on Vis/NIR hyperspectral imaging and discriminant analysis. Journal of Food Measurement & Characterization. 18(1). 560–570. 13 indexed citations
6.
Cubero, Sergio, et al.. (2020). Characterization of a Multispectral Imaging System Based on Narrow Bandwidth Power LEDs. IEEE Transactions on Instrumentation and Measurement. 70. 1–11. 14 indexed citations
7.
Munera, Sandra, J. Blasco, José Manuel Amigo, et al.. (2019). Use of hyperspectral transmittance imaging to evaluate the internal quality of nectarines. Biosystems Engineering. 182. 54–64. 39 indexed citations
8.
Cortés, Victoria, J. Blasco, Nuria Aleixos, Sergio Cubero, & Pau Talens. (2019). Monitoring strategies for quality control of agricultural products using visible and near-infrared spectroscopy: A review. Trends in Food Science & Technology. 85. 138–148. 229 indexed citations
9.
Solaz, Beatriz Rey, Nuria Aleixos, Sergio Cubero, & J. Blasco. (2019). Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing. Remote Sensing. 11(3). 221–221. 34 indexed citations
10.
Blasco, J., et al.. (2019). Dispositivo de captura y envio de imagenes a un servidor remoto para monitorizar trampas para insectos en el campo. Redivia (Instituto Valenciano de Investigaciones Agrarias (IVIA)). 934–939. 1 indexed citations
11.
12.
Blasco, J., Sandra Munera, Nuria Aleixos, Sergio Cubero, & Enrique Moltó. (2017). Machine Vision-Based Measurement Systems for Fruit and Vegetable Quality Control in Postharvest. Advances in biochemical engineering, biotechnology. 161. 71–91. 41 indexed citations
13.
Besada, Cristina, et al.. (2016). El análisis de imagen como herramienta para evaluar de forma objetiva la reacción de los taninos solubles del caqui con el cloruro férrico. Redivia (Instituto Valenciano de Investigaciones Agrarias (IVIA)). 181–185. 1 indexed citations
14.
Cubero, Sergio, et al.. (2016). Automated Systems Based on Machine Vision for Inspecting Citrus Fruits from the Field to Postharvest—a Review. Food and Bioprocess Technology. 9(10). 1623–1639. 91 indexed citations
15.
Tello, Javier, Sergio Cubero, J. Blasco, et al.. (2016). Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters. Journal of the Science of Food and Agriculture. 96(13). 4575–4583. 28 indexed citations
16.
Benalia, Souraya, et al.. (2015). Chemical Engineering Transactions. Redivia (Instituto Valenciano de Investigaciones Agrarias (IVIA)). 6 indexed citations
17.
Cubero, Sergio, et al.. (2010). Real-time inspection of fruit by computer vision on a mobile harvesting platform under field conditions. Redivia (Instituto Valenciano de Investigaciones Agrarias (IVIA)). 6(1). 1–16. 6 indexed citations
18.
Cubero, Sergio, Nuria Aleixos, Enrique Moltó, Juan Gómez‐Sanchís, & J. Blasco. (2010). Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables. Food and Bioprocess Technology. 4(4). 487–504. 373 indexed citations
19.
Blasco, J., et al.. (2008). Short communication.Automatic inspection of the pomegranate (Punica granatum L.) arils quality by means of computer vision. Spanish Journal of Agricultural Research. 6(1). 12–16. 7 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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