Intelligent Agricultural Robots

This special session is organised and supported by the Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil

Session Chairs:

Dr. Antonio Leite, Pontifical Catholic University (PUC-Rio), Brazil

Dr. Marley Vellasco, Pontifical Catholic University (PUC-Rio), Brazil

Dr. Wouter Caarls,Pontifical Catholic University (PUC-Rio), Brazil

Session Committee:

Prof. Paulo Rosa, Instituto Militar de Engenharia (IME), Rio de Janeiro, Brazil
Prof. Ramon R. Costa, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
Prof. Gustavo Medeiros de Freitas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
Prof. Marco Henrique Terra, Universidade de São Paulo (USP), São Carlos (USP-São Carlos), Brazil
Prof. Pal Johan From, Norwegian University of Life Science, As, Norway



Agriculture and food production are facing enormous challenges over the next few years with a growing population and a more challenging climate change. To overcome these difficulties, innovative and intelligent technologies must be developed for a more efficient and sustainable agriculture.

Over the last years, there is a strong trend towards the designing of autonomous robotic systems able to perform a wide range of agricultural tasks in orchards, vineyards, poly-tunnels and farms. For example, precise spot spraying, weeds species recognition and killing, soft-fruit recognition and picking, as well as plant phenotyping are just a few examples of how robots are taking over fields around the world. In general, these robots are equipped with specialized accessories and autonomous navigation systems in order to carry out all the tasks in a safe and efficient manner.

The agricultural environment introduces several challenges, particularly, for robotic harvesting and 3D navigation of mobile robots. Indeed, changes in seasons and weather conditions, crop growth and rotation, dense vegetation, different maturity levels of fruits, the existence of diseases and fungi in plants, all these factors create a dynamic and poorly structured environment. Thus, the automatic harvesting system has to incorporate perception and cognition capabilities in the gripper design as well as intelligent sensors and systems for fruit detection, recognition and localisation.

Considering recent advances in robotic technology around the world, a new trend for agricultural robotics is to combine and integrate advanced control theory, computer vision algorithms, machine learning techniques and visual servoing approaches, allowing robots to perform agricultural tasks with higher level of autonomy and better decision making. In this
context, this special session welcomes papers related to the development of new autonomous farming technologies and precision agricultural robotics.


This special session invites authors to submit high-quality research papers on the topics which include (but are not limited to) the following:

  • Design, modelling and control of agricultural robots
  • Autonomous navigation in farms and orchards
  • Reinforcement learning for task planning
  • Machine learning for computer vision
  • Automated harvesting systems
  • Fruit detection and yield estimation
  • Automated plant phenotyping systems
  • Plant health systems for identification and treatment of diseases
  • Weed and crop pest management
  • Soil, irrigation and pruning management
  • Aerial and ground vehicles for soil/crop monitoring and prediction
  • Small-scale robots for nurseries and greenhouses
  • Remote sensing for precision agriculture
  • Human-robot interaction for agricultural task


Paper Submission:

Prospective authors are invited to submit full-length papers (not exceeding 6 pages) conform to the IEEE format . All papers will be handled and processed electronically via the EDAS online submission system.

Submission implies the willingness of at least one of the authors to register and present their papers.