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How has Rubén Íñiguez's publication output changed over time?
ScholarIQpublication output · 2006–2024
Output grew300% over the shown period — from 1 works in 2006 to 4 in 2024.
1
4
1
4
2006202120232024
What are the most-cited papers on Rubén Íñiguez?
ScholarIQmost cited works
Artificial Intelligence and Novel Sensing Technologies for Assessing Downy Mildew in Grapevine
Inés Hernández, Salvador Gutiérrez, Sara Ceballos, Rubén Íñiguez, Ignacio Barrio, Javier Tardáguila
S2737241975. 202134 CitationsOPEN ACCESS
Assessing and mapping vineyard water status using a ground mobile thermal imaging platform
Salvador Gutiérrez, Juan Fernández‐Novales, María P. Diago, Rubén Íñiguez, Javier Tardáguila
S84263908. 202127 Citations
Impact of Leaf Occlusions on Yield Assessment by Computer Vision in Commercial Vineyards
Rubén Íñiguez, Fernando Palacios, Ignacio Barrio, Inés Hernández, Salvador Gutiérrez, Javier Tardáguila
Agronomy. 202124 CitationsOPEN ACCESS
In-field disease symptom detection and localisation using explainable deep learning: Use case for downy mildew in grapevine
Inés Hernández, Salvador Gutiérrez, Ignacio Barrio, Rubén Íñiguez, Javier Tardáguila
Computers and Electronics in Agriculture. 202422 CitationsOPEN ACCESS
Deep learning modelling for non-invasive grape bunch detection under diverse occlusion conditions
Rubén Íñiguez, Salvador Gutiérrez, Carlos Poblete-Echeverría, Inés Hernández, Ignacio Barrio, Javier Tardáguila
Computers and Electronics in Agriculture. 202410 CitationsOPEN ACCESS
Related on ScholarIQ
Consejo Superior de Investigaciones Científicas
Institution
Artificial Intelligence and Novel Sensing Technologies for Assessing Downy Mildew in Grapevine
Paper
Assessing and mapping vineyard water status using a ground mobile thermal imaging platform
Paper
Impact of Leaf Occlusions on Yield Assessment by Computer Vision in Commercial Vineyards
Paper
In-field disease symptom detection and localisation using explainable deep learning: Use case for downy mildew in grapevine
Paper
Deep learning modelling for non-invasive grape bunch detection under diverse occlusion conditions
Paper