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

Department: PhD in Electrical, Electronics and Communications Engineering
Founded by: Big Data and Data Science Lab (SmartData) of Politecnico di Torino
Supervisor: Prof. Mario Casu (SmartData) | Prof. Marcello Chiaberge (PIC4SeR)
Candidate:  Vittorio Mazzia

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‐toend
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.

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.