Inés Hernández

1.2k total citations
12 papers, 193 citations indexed

About

Inés Hernández is a scholar working on Plant Science, Cell Biology and Ecology. According to data from OpenAlex, Inés Hernández has authored 12 papers receiving a total of 193 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Plant Science, 6 papers in Cell Biology and 3 papers in Ecology. Recurrent topics in Inés Hernández's work include Horticultural and Viticultural Research (10 papers), Smart Agriculture and AI (7 papers) and Plant Pathogens and Fungal Diseases (6 papers). Inés Hernández is often cited by papers focused on Horticultural and Viticultural Research (10 papers), Smart Agriculture and AI (7 papers) and Plant Pathogens and Fungal Diseases (6 papers). Inés Hernández collaborates with scholars based in Spain, South Africa and Italy. Inés Hernández's co-authors include Javier Tardáguila, Salvador Gutiérrez, Sara Ceballos, María P. Diago, Gloria Bueno, Jesús Salido, Ana María Díez-Navajas, I. Santa Regina, Andreina Marques‐Mejias and Santiago Quirce and has published in prestigious journals such as SHILAP Revista de lepidopterología, Computers and Electronics in Agriculture and Scientia Horticulturae.

In The Last Decade

Inés Hernández

10 papers receiving 186 citations

Peers

Inés Hernández

ECOLO

HTM

Leidi Wang China

Kathrin Bürling Germany

Jon Arne Dieseth Norway

Godfree Chigeza Nigeria

Polona Strajnar Slovenia

Ruier Zeng China

J. Van Waes Belgium

Carlos Maldonado Chile

Bangwei Zhou Spain

Thaise Moriwaki Brazil

Leidi Wang China

Inés Hernández

203 ×1.4

93 ×1.7

ECOLO

26 ×0.6

6 ×0.2

2 ×0.1

HTM

Citations per year, relative to Inés Hernández Inés Hernández (= 1×) peers Leidi Wang

Countries citing papers authored by Inés Hernández

Since Specialization

Citations

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

Fields of papers citing papers by Inés Hernández

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Inés Hernández

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

All Works

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12 of 12 papers shown

Poblete-Echeverría, Carlos, et al.. (2025). Using Artificial intelligence for automatic and fast detection of downy mildew symptoms in grapevine canopies. European Journal of Agronomy. 170. 127755–127755.

Hernández, Inés, et al.. (2025). Early detection of downy mildew in vineyards using deep neural networks for semantic segmentation. Biosystems Engineering. 252. 15–31.

Hernández, Inés, et al.. (2024). In-field disease symptom detection and localisation using explainable deep learning: Use case for downy mildew in grapevine. Computers and Electronics in Agriculture. 226. 109478–109478. 7 indexed citations

Hernández, Inés, Salvador Gutiérrez, & Javier Tardáguila. (2024). Image analysis with deep learning for early detection of downy mildew in grapevine. Scientia Horticulturae. 331. 113155–113155. 8 indexed citations

Poblete-Echeverría, Carlos, et al.. (2023). Using artificial intelligence (AI) for grapevine disease detection based on images. SHILAP Revista de lepidopterología. 68. 1021–1021. 2 indexed citations

Hernández, Inés, Salvador Gutiérrez, Sara Ceballos, et al.. (2022). Assessment of downy mildew in grapevine using computer vision and fuzzy logic. Development and validation of a new method. OENO One. 56(3). 41–53. 11 indexed citations

Hernández, Inés, et al.. (2021). Impact of Leaf Occlusions on Yield Assessment by Computer Vision in Commercial Vineyards. Agronomy. 11(5). 1003–1003. 14 indexed citations

Gutiérrez, Salvador, et al.. (2021). Deep learning for the differentiation of downy mildew and spider mite in grapevine under field conditions. Computers and Electronics in Agriculture. 182. 105991–105991. 42 indexed citations

Hernández, Inés, et al.. (2021). Artificial Intelligence and Novel Sensing Technologies for Assessing Downy Mildew in Grapevine. Horticulturae. 7(5). 103–103. 23 indexed citations

10.

Bueno, Gloria, et al.. (2020). Automated grapevine flower detection and quantification method based on computer vision and deep learning from on-the-go imaging using a mobile sensing platform under field conditions. Computers and Electronics in Agriculture. 178. 105796–105796. 54 indexed citations

11.

Marques‐Mejias, Andreina, et al.. (2018). Asthma Exacerbations in the Pediatric Emergency Department at a Tertiary Hospital: Association With Environmental Factors. Journal of Investigational Allergology and Clinical Immunology. 29(5). 365–370. 16 indexed citations

12.

Hernández, Inés, et al.. (1992). Dinámica de la descomposición de la hojarasca forestal en bosques de la cuenca del duero (provincia de zamora): Modelización de la pérdida de peso. Arid Soil Research and Rehabilitation. 6(4). 339–355. 16 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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