Mozambique
- Daniel Nunes
- ICC Educação Continuada
Intermediate
- Bank transfer
- Paypal
Event Organizer(s)
Description
The Smart Agriculture Solutions for Agribusiness Advancement course is a comprehensive program focused on understanding and implementing Internet of Things solutions in the agricultural context. Participants will be introduced to the fundamental principles of IoT and explore its potential via data collection, device connectivity, and sensor application for optimizing agricultural processes. Delving into communication protocols, participants will be empowered to develop and deploy intelligent systems for agricultural monitoring, crop management, asset tracking, and remote operation control, aiming to increase efficiency and productivity in the rural environment.
This course is intended for decision-makers, policy-makers and professionals involved in agribusiness modernization, including agronomists, agricultural engineers, farm managers, consultants, technical specialists, IoT and automation professionals, researchers, and AgTech entrepreneurs.
Members of the above-mentioned target population are invited to apply for the training if they meet the following criteria:
- Have basic knowledge of ICT and/or technology-related issues and the ability to understand the course topics.
The number of available places is limited to 30.
Upon completion of this course, participants will be able to:
- Describe the core concepts of the Internet of Things, including how devices communicate and interact within a network to collect and exchange data;
- Discuss how IoT technologies can be applied to various aspects of agriculture, such as soil care, irrigation, crop management, livestock monitoring, and precision farming, to improve efficiency and productivity;
- Differentiate the sensors, devices, and platforms used in IoT systems, including their functionalities and how they can be applied in agricultural contexts;
- Compare with various communication technologies and protocols suitable for rural environments, ensuring reliable data transmission between devices and servers;
- Analyze successful IoT implementations in agriculture, including the challenges faced and solutions developed, providing a practical perspective on how IoT can be effectively utilized.
To achieve the training objectives described, the course content will be delivered using the online instructor-led methodology, allowing the participants to access the course from anywhere, offering flexibility and convenience. Live sessions will be conducted through the Zoom platform, facilitating direct interaction between instructors and participants, as well as among the participants themselves. This approach promotes real-time discussions, group work, and instant feedback, maintaining a dynamic and interactive learning environment.
The live lecture will take place as per the schedule below:
- Lecture one: July 07, 2026 from 13:30 to 16:30 (CEST)
- Lecture two: July 09, 2026 from 13:30 to 16:30 (CEST)
- Lecture three: July 14, 2026 from 13:30 to 16:30 (CEST)
- Lecture four: July 16, 2026 from 13:30 to 16:30 (CEST)
- Individual assignment submission and the final test deadline: 23 July 2026 23:59 (CEST)
The assessment and grading methodology will include:
- A final test with 20 multiple-choice questions
- An individual assignment submission
- Active participation in the live lectures
The final test will contain 20 questions to provide a more comprehensive assessment. The questions are evenly distributed across the four module topics, with 5 questions dedicated to each topic, ensuring thorough coverage. To enhance the variety and challenge, the quiz includes both Multiple Choice, Single Answer (MC-SA) and Multiple Choice, Multiple Answer (MC-MA) formats. Specifically, at least 5 out of the 20 questions are MC-MA, requiring the selection of multiple correct answers. Additionally, the order of the questions is randomized to prevent predictability and encourage a more robust evaluation of the students' understanding.
In addition to the final test, participants are also required to complete an individual assignment selecting among one of four themes, one for each module of the course. In this assignment, participants are asked to analyze the content of each module in the context of their organization, industry or country/region.
The proposed themes for the individual assignment are described below. The participant should choose one of them.
Topic 1: IoT for Precision Farming
Objective: Evaluate how IoT technologies can support precision farming practices in a regional agricultural context.
Assignment Description: Analyze a crop or agricultural production system relevant to your region and propose how IoT devices, sensors, and data collection mechanisms could be used to monitor field conditions, optimize resource utilization, and improve productivity.
Deliverable: A 300–500 word document describing the agricultural scenario, the proposed IoT solution, expected benefits, and implementation considerations.
Topic 2: Smart Irrigation for Sustainable Water Management
Objective: Assess the potential of IoT-enabled irrigation systems to improve water management in agriculture.
Assignment Description: Identify a local agricultural activity where water availability or irrigation efficiency is a significant concern. Describe how soil moisture sensors, weather monitoring systems, and connected irrigation technologies could be integrated to optimize water usage and support sustainable farming practices.
Deliverable: A 300–500 word document outlining the regional challenge, the proposed IoT-based irrigation approach, and the anticipated operational and environmental benefits.
Topic 3: Connectivity Challenges and IoT Deployment in Rural Areas
Objective: Analyze the connectivity requirements and technology options for implementing IoT solutions in agricultural environments.
Assignment Description: Evaluate the connectivity conditions of a rural area in your region and discuss the suitability of technologies such as LoRaWAN, NB-IoT, cellular networks, or satellite communications for supporting agricultural IoT applications. Consider coverage, cost, scalability, and operational requirements.
Deliverable: A 300–500 word document describing the regional context, connectivity challenges, recommended communication technologies, and justification for the proposed solution.
Topic 4: Future Trends in Smart Agriculture
Objective: Explore emerging technologies and future opportunities for digital transformation in agriculture.
Assignment Description: Investigate how IoT, Artificial Intelligence (AI), Machine Learning (ML), edge computing, or advanced analytics could address current agricultural challenges in your region. Discuss potential applications, expected benefits, and possible barriers to adoption.
Deliverable: A 300–500 word document presenting the selected technology trends, their relevance to the regional agricultural sector, and their potential impact on future agricultural operations.
The grades will be distributed based on the following grading scale:
Activity Weighting (%)
- Final test: 40%
- Individual assignment: 40%
- Active participation in the live lectures: 20%
- Total: 100%
Session 1: Introduction to Smart Agriculture and IoT Applications
- Introduction to IoT in Agriculture
- Precision Farming
- Smart Irrigation Systems
Session 2: Agricultural IoT Devices, Sensors and Connectivity
- Livestock Monitoring
- IoT Devices and Sensors
- Connectivity Solutions
Session 3: IoT Platforms, Data Management and Security
- IoT Platforms and Tools
- Security and Privacy
Session 4: Real-World Applications and Future Trends
- Case Studies
- Lessons Learned
- Innovative and Future Trends
Tutors
Registration information
Unless specified otherwise, all ITU Academy training courses are open to all interested professionals, irrespective of their race, ethnicity, age, gender, religion, economic status and other diverse backgrounds. We strongly encourage registrations from female participants, and participants from developing countries. This includes least developed countries, small island developing states and landlocked developing countries.
