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Semestre (Sem)
1	Primo Semestre
2	Secondo Semestre
A	Insegnamento Annuale
Attivit� formative
C	Affini o integrative
B	Caratterizzanti
Lingua d'erogazione
	Insegnamento completamente offerto in lingua italiana
	Insegnamento completamente offerto in lingua inglese
--	Non definita
Didattica innovativa
	I CFU riportati a fianco a questo simbolo indicano la parte dei CFU dell'insegnamento erogati con Didattica Innovativa. Tali CFU riguardano: Cotutela con mondo esterno Blended Learning & Flipped Classroom Massive Open Online Courses (MOOC) Soft Skills

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Contesto

Anno Accademico	2022/2023
Scuola	Scuola di Ingegneria Industriale e dell'Informazione
Corso di Studi	(Mag.)(ord. 270) - BV (479) Management Engineering - Ingegneria Gestionale
Piano di Studio preventivamente approvato	IND - INDUSTRIAL MANAGEMENT
Anno di Corso	2

Scheda Insegnamento

Codice Identificativo

057498

Denominazione Insegnamento

DATA ANALYTICS FOR SMART AGRICULTURE

Tipo Insegnamento

Corso Integrato

Crediti Formativi Universitari (CFU)

5.0

Semestre

Primo Semestre

Programma sintetico

The course goal is to present a practical overview of data gathering, managing, and exploitation in Climate-Smart Agriculture (CSA). The concept of Climate-Smart Agriculture has been defined by the Food and Agriculture Organization of the United Nations as "a strategy to address the challenges of climate change and food security by sustainably increasing productivity, bolstering resilience, reducing GHG emissions, and enhancing the achievement of national security and development goals" [1]. CSA is the implementation of the ""Zero hunger"" Sustainable Development Goals of the United Nations. The advent of digital technologies in agriculture has shaped the CSA concept giving birth to new terms like Smart Agriculture, Digital Agriculture, and Agriculture 4.0. Digital technologies like Artificial Intelligence (AI), Robotics, and the Internet of Things are expected to be game-changers in achieving the CSA objectives. Digital technologies allow for detailed real-time analysis of data from smart sensors, ground vehicles, aerial drones, or satellites. This big amount of data is analyzed by machine learning techniques to produce information upon which farmers can make decisions instead of entirely relying on their personal beliefs. These technologies allow increasing productivity while decreasing costs and being more environmentally friendly. In the course, we will approach the whole value chain of data in Agriculture 4.0 starting from the means to acquire information via IoT sensors, aerial imaging, remote sensing, and auxiliary sources such as agrometeo and field surveys, then we will discuss the most common techniques for data processing and the tools to perform such processing and finally we will present how data can be turned into an actionable source of information discussing the way it can impact the agri-food value chain. This course is intended as a Master Level class on data analytics; basic notions of mathematics, statistics, databases, and (object-oriented) programming are assumed as pre-requirement for a successful attendance of the course. [1] A. Chandra, K. E. McNamara, and P. Dargusch, "Climate-smart agriculture: Perspectives and framings," Climate Policy, vol. 18, no. 4, pp. 526-541, 2018.

Settori Scientifico Disciplinari (SSD)