Machine Learning algorithms and their embedded implementation for service robotics applications in precision agriculture

PhD Program in Electrical, Electronics and Communications Engineering


Mario Casu –
Marcello Chiaberge –

Context of the research activity

Several studies have demonstrated the need to significantly increase the world’s food production by 2050. Technology could help the farmer, its adoption is limited because the farms usually do not have power, or Internet connectivity, and the farmers are typically not technology savvy. We are working towards an end‐to‐ end approach, from sensors to the cloud, to solve the problem. Our goal is to enable data‐driven precision farming. We believe that data, coupled with the farmer’s knowledge and intuition about his or her farm, can help increase farm productivity, and also help reduce costs. However, getting data from the farm is extremely difficult since there is often no power in the field, or Internet in the farms. As part of the PIC4SeR project, we are developing several unique solutions to solve these problems using low‐cost sensors, drones, rovers, vision analysis and machine learning algorithms. The research activity fits in the SmartData@PoliTo interdepartmental centre, that brings together competences from different fields, ranging from modelling to computer programming, from communications to statistics. The candidate will join this interdisciplinary team of experts and collaborate with them.


The first task of the research activity will be dedicated to the realization of a set of algorithms and methodologies dedicated for data analysis in the field of precision agriculture. The target is the design and development of a modular and flexible software implementation able to run on general purpose processors or cluster systems. Among the main advantages of such implementation strategy, it is worth mentioning the flexibility of the architecture and the possibility to interact with the low level layers of the robotic control architecture (based on ROS). This approach is strongly innovative since at present such a modular and hierarchical architecture is still not available. The second task will be dedicated to the implementation of embedded solutions able to run the previous developed algorithms using and integrating also available or newly developed hardware neural/genetic processors and accelerators.

Skills and competencies for the development of the activity


  • Knowledge of advanced processing techniques
  • Machine learning fundamentals
  • Digital design

Skills and tools:

  • C/C++/Python programming
  • Matlab
  • Embedded system design


Further information about the PhD program at Politecnico can be found here

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