Luis Emmi

About

Luis Emmi is a prominent robotics and precision agriculture researcher whose work sits at the intersection of autonomous systems, mobile robotics, and sustainable farming technologies. Based on a body of highly cited publications spanning over a decade, Emmi has made foundational contributions to the development and deployment of autonomous robotic fleets for agricultural applications, with his 2014 paper on real fleet integration accumulating 153 citations and his 2020 work on intelligent field robots drawing 148. Emmi's research consistently addresses the practical challenges of deploying autonomous systems in unstructured outdoor environments, from advancing machine vision selection frameworks and GNSS positioning accuracy to designing hybrid-powered robotic tractors that measurably reduce fuel consumption and air pollution. His work on autonomous patch sprayers and weed control robots demonstrates a strong commitment to environmental sustainability alongside operational efficiency. More recently, Emmi has explored cutting-edge frontiers including IoT and cloud-integrated agricultural robots and improved navigation through hybrid environmental representations. With a cumulative citation count exceeding 800 across his top works, his research has shaped how the agricultural robotics community approaches scalability, precision, and intelligence in farm automation — making his publications essential reading for students entering this rapidly evolving field.

Research Focus

Key Achievements

Top Papers

1. 1
   New Trends in Robotics for Agriculture: Integration and Assessment of a Real Fleet of Robots

   153 citations · 2014
2. 2
   Field Robots for Intelligent Farms—Inhering Features from Industry

   148 citations · 2020
3. 3
   Autonomous systems for precise spraying – Evaluation of a robotised patch sprayer

   132 citations · 2016
4. 4
   Reducing air pollution with hybrid-powered robotic tractors for precision agriculture

   104 citations · 2016
5. 5
   A hybrid representation of the environment to improve autonomous navigation of mobile robots in agriculture

   59 citations · 2021
6. 6
   Reducing fuel consumption in weed and pest control using robotic tractors

   55 citations · 2015
7. 7
   Machine-Vision Systems Selection for Agricultural Vehicles: A Guide

   49 citations · 2016
8. 8
   Fleets of robots for precision agriculture: a simulation environment

   44 citations · 2013
9. 9
   Comparison of Positional Accuracy betweenRTK and RTX GNSS Based on the Autonomous Agricultural Vehicles under Field Conditions

   42 citations · 2014
10. 10
    Exploiting the Internet Resources for Autonomous Robots in Agriculture

    34 citations · 2023

Key Collaborators

Contact & Links