Application Systems

Application Systems

This Learning Object is part of Master AGRITECH EU – Digital Agriculture for Sustainable Development, an Italian one-year specialisation programme organised by University of Pisa, University of Macerata, National Research Council (CNR) and Quinn Consortium (Consorzio Quinn)

For more information about the programme and to enroll here

It is possible to enroll for the whole programme or for the single Learning Object (Module) you are interested in

Module details

The module covers the core application systems supporting precision agriculture, starting with GNSS and localisation technologies. Students will explore the concept of interoperability and the role of standards in ensuring seamless communication between heterogeneous platforms. The module also examines the evolution of operating systems and the development of applications within both web and Android environments.


Proposer: University of Pisa (UNIPI)

Organization: University of Pisa (UNIPI), National Reasearch Council (CNR), University of Macerata (UNIMC)

Duration: 22h

ECTS: 5

Shortcut access code: No

Year of pubblication: 2026

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Delivery methods: On Line

Teaching methods: Lectures, Working Group

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Learning objectives
  • GNSS and positioning systems
  • Concept of interoperability
  • The standards
  • The world of the web and Android
  • The evolution of operating systems.
     
  • IoT hardware for agriculture

    Deploying an instance of NodeRed for data presentation, connecting to the external gateway, deploying specific sensor for agriculture


    Description: This unit provides a hands-on introduction to IoT hardware components — including microcontrollers, sensors, and connectivity modules — and their practical deployment in agricultural monitoring and automation systems.
    Duration: 8h
    Teacher: La Rosa (CNR)
    Delivery method: On Line
    Teaching method: Lectures, Working Group

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  • Exploratory data analysis

    An agricultural dashboard based on NodeRed for visualization and automating tasks


    Description: This unit introduces exploratory data analysis techniques to uncover patterns, detect anomalies, and extract preliminary insights from agricultural datasets, laying the groundwork for more advanced modelling and decision-making.
    Duration: 14h
    Teacher: La Rosa (CNR)
    Delivery method: On Line
    Teaching method: Lectures, Working Group

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