This Learning Object is part of the improvements to Spanish Master’s and Bachelor degrees in Agricultural and Industrial Engineering fields developed within AGRITECH EU project. New content and methodological approaches involve sustainability (e.g. warehouse, conceptual models, data analysis, labour productivity, LCA), automation (VR & AR) and application scenarios (VR & AR, virtual and interactive tools)
For more information about the programmes click here
Module details
This module equips students to understand agricultural digitalisation through the use of the Internet of Things (IoT) and cyber-physical systems, including data acquisition, sensors and connected devices; to understand and compare the main communication protocols used in agricultural settings and their integration into cyber-physical systems (CPS); apply concepts of cloud computing and data analytics for the management and exploitation of agricultural information. Students will learn about the main digital platforms and decision support tools used in intensive production, concluding with a practical project that integrates IoT, CPS, data analysis and DSS in a real-world scenario.
Proposer: University of Almeria (UAL)
Organization: Universidad de Almeria (UAL)
Duration: 30h
ECTS: 6
Tools required:
Arduino uno, Tinkercad
Docker, Node-red, Openweathermap
Arduino Uno, Tinkercad, Docker, Node-red, Openweathermap
Shortcut access code: No
Year of pubblication: 2026
Topics
Tags: Agritech, DSS, greenhouse automation, IoT, sensors
Subject areas: Communication and Cyber-physical Systems
Delivery methods: On Line
Teaching methods: Lectures, Project Work
Learning objectives
- Provide a concise overview of digitalisation and the IoT as applied to intensive agriculture, covering how these technologies improve greenhouse monitoring and management
- Cyber-physical systems and communication protocols most commonly used in the sector
- Fundamentals of cloud computing and agricultural data analysis
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Digitalization and the Internet of Things (IoT) in Agriculture
The course covers the basic components of IoT systems and how connected devices support monitoring, automation, and decision‑making in modern farming.
Description: This unit explores the role of digital technologies and the Internet of Things in transforming agriculture. Students learn how sensors, connectivity, and real‑time data collection improve crop and livestock monitoring, optimize resource use, and automate key farm operations. The unit also highlights practical applications such as smart irrigation, environmental monitoring, and predictive analytics, as well as challenges related to connectivity, data management, and system integration.Duration: 5hTeacher: Manuel Muñoz RodríguezDelivery method: On LineTeaching method: Lectures -
Cyber-Physical Systems (CPS) and Communications
Introduction to the fundamentals of Cyber‑Physical Systems (CPS) and industrial communication technologies
Description: This unit explores the structure and operation of Cyber‑Physical Systems, focusing on how embedded sensors, actuators, and control algorithms integrate with digital communication networks. Students learn the principles of connectivity, including fieldbuses, industrial Ethernet, and wireless communication used to link devices and systems. The unit highlights applications such as remote monitoring, smart equipment coordination, data exchange between machines, and secure, reliable communication essential for advanced automation in agriculture and industry.Duration: 5hTeacher: Manuel Muñoz RodríguezDelivery method: On LineTeaching method: LecturesRequired tools: Arduino uno, Tinkercad -
Cloud Computing and Data Analytics
Introduction to the basics of cloud computing and data analytics, focusing on how cloud platforms enable scalable storage, processing, and analysis of agricultural data
Description: This unit explores how cloud computing and data analytics enhance agricultural management by providing flexible data storage, remote access, and powerful analytical tools. It explains how data from sensors, machines, and farm systems can be processed in the cloud to generate insights, optimize operations, and support predictive decision‑making. Students also learn about key concepts such as cloud architectures, big‑data workflows, dashboards, and data‑driven strategies for improving productivity and sustainability.Duration: 5hTeacher: Manuel Muñoz RodríguezDelivery method: On LineTeaching method: LecturesRequired tools: Docker, Node-red, Openweathermap
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Applications, digital platforms and DSS tools
This unit introduces the main digital applications, platforms, and decision‑support tools (DSS) used in modern agriculture
Description: This unit introduces the main digital applications, platforms, and decision‑support tools (DSS) used in modern agriculture. It explains how these tools integrate data, automate tasks, and assist farmers in making informed and efficient decisions.The unit also highlights practical examples such as task‑management apps, crop‑monitoring platforms, and decision‑support dashboards that enhance productivity and sustainability.Duration: 5hTeacher: Manuel Muñoz RodríguezDelivery method: On LineTeaching method: Lectures -
Final project
This unit focuses on applying the knowledge acquired throughout the course to a practical final project
Description: In this unit, students work independently or in small groups to develop a final project that integrates concepts such as automation, IoT, digital platforms, data analytics, or decision‑support tools. The project may involve designing a system, creating a prototype, analyzing a real case, or proposing an innovative solution to a real agricultural or industrial challenge. Students document their work, justify their decisions, and present the final outcomes.Duration: 10hTeacher: Manuel Muñoz RodríguezDelivery method: On LineTeaching method: Project WorkRequired tools: Arduino Uno, Tinkercad, Docker, Node-red, Openweathermap