- Docente: Lorenzo Marconi
- Crediti formativi: 6
- SSD: ING-INF/04
- Lingua di insegnamento: Inglese
- Modalità didattica: Convenzionale - Lezioni in presenza
- Campus: Bologna
- Corso: Laurea Magistrale in Precise and sustainable agriculture (cod. 5705)
Conoscenze e abilità da conseguire
At the end of the course, the student possesses the basic knowledge of the main technologies: to monitor environmental, soil and crop data; for remote transmission and storage of digital data; of leading land and air robotic platforms, concerning precision agriculture applications. In particular, the student possesses the skills to: evaluate the feasibility of instrumental solutions for field activities and automatic outdoor monitoring, both in relation to technological and economic aspects; speak proficiently with expert designers when determining the settings of technological systems applied in precision agriculture.
Contenuti
1. Introduction to precision agriculture and sensing technologies (ca. 15 hrs)
Introduction to the course and to the Precision Agriculture concept. Overview of the general architecture, suitable to gather relevant data, process it and suggest or take proper decisions in fields or orchards. Different types of sensors overview, data acquisition methods and Smart Objects.
2. Deployment and robotic applications (ca. 15 hrs)
Sensor deployment, hands-on laboratory and robotic autonomous platform overview. Data models and on-board sensors for autonomous operation.
3. Control systems basics and ground rover control algorithms and navigation (ca. 15 hrs)
Introduction to control systems, dynamic systems representation, stability of linear systems, reachability and controllability, simple regulators synthesis. Overview of existing navigation algorithms and mission planning and execution.
4. Field work and B.I. analytics and commercial systems overview (ca. 12 hrs)
Decision Support Systems and existing data analytics software overview. Hands-on laboratory
6. Seminar* (3 hrs)
Delivered by a supplier of IoT services for agriculture.
* To be held in collaboration with the course IoT and Big Data for smart agricultural systems
Testi/Bibliografia
A. Castrignano, G. Buttafuoco, R. K. Abdul Mouazen, D. Moshou, O. Naud, “Agricultural Internet of Things and Decision Support for Precision Smart Farming”, Academic Press
X. E. Pantazi, D. Moshou and D. Bochtis, “Intelligent Data Mining and Fusion Systems in Agriculture”, Academic PressMetodi didattici
Slides (available on "insegnamenti online")
Matlab-Simulink
Node-REDModalità di verifica e valutazione dell'apprendimento
Oral examination with project discussion.
Strumenti a supporto della didattica
Numerical simulation tool using matlab/simulink
IOT development tools using Node-RED.
Orario di ricevimento
Consulta il sito web di Lorenzo Marconi