Agrofood and Environment Sustainability

The CU seeks to develop doctoral students’ in-depth knowledge of the different approaches and technologies and how they can be integrated for agricultural management. In addition to knowledge and proficiency from the user’s perspective, PhD students will also have the chance to deepen their technical knowledge through technological challenges, aligned with their thesis topic and aimed at acquiring skills that will be used and optimized in their thesis work. This Problem Based Learning approach allows them to deepen their knowledge and, above all, their technical skills, which are unusual in the field of agriculture.

The proposed methodologies include the exposition of concepts and theoretical content, followed by “hands on”, “on the job” opportunities individually targeted to the needs in terms of the doctoral thesis of each of the PhD students.

Thus, the approach seeks to make doctoral students proficient in remote sensing, image processing and the use of GIS as a tool to support decision-making.

PhD students will also acquire skills in IoT, automation, robotic systems and data processing using artificial intelligence and machine learning methodologies.

Theoretical lectures cover each subject. Theoretical-practical classes combine theoretical concepts with the resolution of theoretical/practical exercises and the presentation of equipment used in operations. In the practical component, PhD students will develop their own project, in line with the theme of their doctorate, i.e. they will develop skills that will later be used in their thesis work.

The course provides fundamental knowledge of precision agriculture and automation in agriculture, and the use of information systems in agricultural management. PhD students should: 

Be proficient in the use of Geographic Information Systems (GIS) software and the use of GIS software for editing, integrate data and produce information.

Acquires know how to process and edit images, record, process data and graphically data representation. 

Know the structure, functions and connections of different types of sensors and actuators, and IoT devices and technologies.

Differentiate between various automation systems and identify the components used, as well as understanding the ideas behind automation technology and recognizing the different types of robotic systems.

Explore the tools and systems that use artificial intelligence (AI), as well as learning about the development of machine learning techniques, with real data.

Digital Agriculture
Agriculture 4.0 and Precision Farming. Use of technology to optimize resources such as water, fertilizers and pesticides.
Satellite and drone technology applied to agriculture
Global Navigation Satellite Systems (GNSS) and the use of drones. Remote sensing applied to agriculture. Digital image processing and interpretation for monitoring agricultural crops.
Geographic Information Systems (GIS) in Agriculture IoT, Automation and Agricultural Robotics
Internet of Things (IoT); robotic and autonomous systems; vision systems; automation; use of robots and automated machines in the field.
Artificial Intelligence, Machine Learning and Big Data in Agriculture
Storing, analyzing and interpreting large volumes of data (Big Data). Application of AI and machine learning techniques in agricultural data analysis. Automated decision-making and forecasting.
Blockchain in Agriculture
Traceability and transparency in the agricultural product chain.

NAO