93654 - CROP SENSING, DATA HARVESTING AND ROBOT TECHNOLOGIES FOR PRECISION AGRICULTURE

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. Ground and aerial robotics capabilities and basics in navigation algorithm (ca. 15 hrs)

Overview of autonomous/semiautonomous robotic platforms for agriculture applications. Potential payload, endurance, navigation capabilities and application contexts. 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 Press

Metodi didattici

Slides (available on "insegnamenti online")

Matlab-Simulink

Node-RED

Modalità 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

Consulta il sito web di Dario Mengoli