With this function you can construct your weekly calendar of lessons, which is customized on the basis of the courses that you intend to follow. Warning: the personal schedule does not replace the presentation of the study plan! It's an informal tool that can help you better manage the organization of class attendance before the study plan presentation. After the study plan presentation we recommend you to use the Lecture timetable service in your Online Services.
To create your customized schedule follow these instructions:
- Click on the "Enable" link to proceed. You will be asked your surname and first name in order to determine your alphabetic grouping.
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To add or remove courses from your personal schedule, use the small icons which are found next to the courses:
addition of the course
removal of the course
selection of the section of the Laboratory of Architecture (Note: the effective area in which the teaching will be carried out will be determined after the presentation of the Study Plans)
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The sidebar on the left displays the number of lessons included in schedule.
There are also these commands:
View the schedule: allows the viewing of the weekly synoptic schedule
Delete the schedule: cancels the selections made
When you have finished the entry, you can print the calendar you have made.
Semester (Sem) | 1 | First Semester | 2 | Second Semester | A | Annual course | Educational activities | C | Similar or integrative activities | B | Identifying activities | Language |  | Course completely offered in italian |  | Course completely offered in english | -- | Not available | Innovative teaching |  | The credits shown next to this symbol indicate the part of the course CFUs provided with Innovative teaching. These CFUs include:
- Subject taught jointly with companies or organizations
- Blended Learning & Flipped Classroom
- Massive Open Online Courses (MOOC)
- Soft Skills
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Academic Year
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2022/2023
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School
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School of Industrial and Information Engineering
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Name
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(Master of Science degree)(ord. 270) - BV (479) Management Engineering
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Track
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IND - INDUSTRIAL MANAGEMENT
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Programme Year
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2
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ID Code
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057498
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Course Title
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DATA ANALYTICS FOR SMART AGRICULTURE
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Course Type
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Integrated Course
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Credits (CFU / ECTS)
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5.0
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Semester
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First Semester
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Course Description
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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.
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Scientific-Disciplinary Sector (SSD)
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Educational activities
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SSD Code
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SSD Description
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CFU
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C
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ING-INF/05
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INFORMATION PROCESSING SYSTEMS
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3.0
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B,C
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ING-IND/17
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INDUSTRIAL MECHANICAL SYSTEMS ENGINEERING
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2.0
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