93653 - Iot And Big Data For Smart Agricultural Systems

Course Unit Page

Academic Year 2022/2023

Learning outcomes

At the end of the course, the student possesses knowledge on: the basics for ICT, big data and IoT; the introduction to IoT, application scenarios, enabling definitions and technologies, cloud and fog computing; the main components of IoT solutions, big data and references to Artificial Intelligence; IoT and big data services from the product to the service, and application cases in smart agriculture. The Graduate possesses the skills to: analyze and organize small big data and IoT projects for smart agriculture solutions.

Course contents

1. Computer basics

Introduction to Computer Science. Computer architecture. Digital representation of information.

2. Internet of Things (IoT)

Recall from Sensors operating principle. Smart Sensors. Data transfer technologies employed in open-field agriculture. Infrastructures for data elaboration: Cloud Computing. Examples of IoT applications in agriculture: monitoring soil properties, weather condition, crops.

3. Big Data and Artificial Intelligence

Definition of Big Data. Parallel computing. Artificial Intelligence (AI) and Machine Learning. Touch on AI and Big Data tools. Examples of Big Data/AI applications in agriculture.

4. Tools for data analysis, elaboration and visualizations

Data analytics and visualization: Tableau. Hands-on laboratory

5. Field work*

Design and implementation of two IoT practical applications in a sample plot of land: Arboriculture and Extensive Culture.

6. Seminar*

Delivered by a supplier of IoT services for agriculture.

* Held in collaboration with the course Crop Sensing, Data Harvesting and Robot Technologies for Precision Agriculture


The course material is composed of:

  • Lecture notes of the teacher

  • Scientific papers recommended by the teacher to deepen relevant aspects





Teaching methods

Course contents are presented in class. For verification purpose, learning check points are envisaged at the end of each macro-topic. Some classes are hands-on on specific topics in the lab to go into the practical knowledge. IoT practical applications will be implemented in a sample plot of land. At the end of the course, a seminar will be given by an industrial party. Students are referred to papers and articles to choose from and encouraged to individually deep into some topics.

Assessment methods

The final test consists of:

a) explanation and presentation of the solution to a data visualization problem using the Tableau software tool

b) a colloquium addressing the most important principles and issues in the following areas: Computer basics, IoT and Big Data for smart agriculture

c) (optional) oral one-on-one discussion of a scientific paper

Test duration: 20 to 30 minutes

Marking scheme:

- Up to 50% marks can be earned in point a)

- Up to 50% marks can be earned in point b)

- Extra marks (up to 10%) can be earned in c)

Teaching tools

In-class discussion of the course topics are presented with the support of Power Point slides.

Students will use a licensed software tool (Tableau) to implement practical data manipulation and visualization examples.

Office hours

See the website of Giuseppe Di Modica