The course covers topics related to the circular economy as a new concept for dealing with problems related to resource scarcity, economic sustainability and environmental degradation. Additionally, within the course green ICTs are analyzed with a special focus on smart agriculture.
Course Overview Table
| Chapter | Details |
| Partner | University “St. Kliment Ohridski” – Bitola, Faculty of Information and Communication Technologies |
| Title | Smart Agriculture and IoT |
| Service | Course |
| Target Group | Small and medium-sized agricultural companies, individual farmers, employees in public administration, and startups. |
| Format | Workshop including in-person training. |
| Focused on Key Technologies | Internet of Things (IoT), Composting, application of Artificial Intelligence (AI) in agriculture, and Crowdfunding platforms. |
| Status | Ready to offer. |
| Stakeholders from SME/PA Side | Farmers, potential farmers, agricultural advisors, food inspectors, environmental inspectors, agricultural entrepreneurs, ICT entrepreneurs. |
| Requirements for Participation | None. |
| Estimated Duration | 16 hours |
Course Description: Smart Agriculture and IoT
Introduction
Unlock the future of farming with our course on Smart Agriculture and IoT. Designed for farmers, agritech enthusiasts, and tech professionals, this course explores how Internet of Things (IoT) technologies revolutionize agriculture. Learn to optimize resources, boost yields, and promote sustainability through hands-on, practical insights.
Technical Context and Examples
Smart Agriculture leverages IoT to transform traditional farming into data-driven practices. Sensors monitor soil moisture, temperature, and crop health, while drones provide aerial insights for precision farming. For example, IoT-enabled irrigation systems adjust water usage based on real-time weather data, saving up to 30% of water. Automated machinery, like robotic harvesters, reduces labor costs and increases efficiency. This course delves into platforms like LoRaWAN for long-range communication and cloud-based analytics for decision-making, showcasing real-world applications such as smart greenhouses that maintain optimal conditions for plant growth.
Detailed Explanation of Core Concepts
The course covers IoT fundamentals, including sensor networks, data collection, and connectivity protocols like MQTT and Zigbee. You’ll explore how these technologies integrate with agriculture to enable precision farming—optimizing water, fertilizers, and pesticides. Learn about data analytics for predictive insights, such as forecasting pest outbreaks or crop yields. Practical sessions include setting up IoT devices, interpreting sensor data, and using AI tools for farm management. By understanding these core concepts, you’ll be equipped to implement scalable, tech-driven solutions that enhance productivity while minimizing environmental impact.
Tentative Agenda of the Course
Introduction to IoT and Smart Agriculture (2 hours);
Sensor Technology and Data Collection (2 hours);
Hands-on: Setting Up IoT Devices (2 hours);
Case Studies in Precision Farming (2 hours);
Connectivity Protocols and Cloud Integration (2 hours);
Data Analytics for Agriculture (2 hours);
Practical: Building a Smart Irrigation System (2 hours) and
Future Trends and Project Discussion (2 hours).
Conclusion and Unique Value
This course empowers you to integrate IoT into agriculture, driving efficiency and sustainability. With hands-on projects and expert insights, you’ll gain skills to implement smart farming solutions. Join us to be at the forefront of agritech innovation, transforming farming practices for a better future while staying ahead in this rapidly evolving field.
Additional Course Information
| Category | Details |
|
Developed skills |
Participants will acquire knowledge and skills, including: |
| – IoT Implementation: Setting up and configuring IoT devices like sensors and actuators for agricultural use. – Data Analysis: Interpreting sensor data to make informed decisions on irrigation, fertilization, and pest control. – Precision Farming Techniques: Applying technology to optimize resources and increase crop yields sustainably. – Connectivity and Integration: Using protocols like MQTT and Zigbee to connect devices and integrate with cloud platforms. – Predictive Analytics: Leveraging AI tools to forecast agricultural outcomes, such as pest outbreaks or harvest yields. – Problem-solving: Designing and troubleshooting smart farming systems, like automated irrigation setups. – Sustainability Practices: Implementing tech-driven solutions to reduce environmental impact while enhancing productivity. | |
| Learning Methods Used | Lectures, practical work in groups (exercises, group discussions, case studies), digital simulations, etc. |
| References/Resources | “Internet of Things for Agriculture 4.0: Impact and Challenges” by P.S. Ranjit et al. (Taylor & Francis, 2022) |
| Overview Slides | In preparation |