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This course provides computer science and agriculture students with an introduction to solving problems in the agriculture and food domains through applied data analytics and artificial intelligence principles, enabling them to engage with the practical challenges of farming while exploring the synergy between computer science and agriculture.

The course begins by establishing a foundation in applied data analytics tailored to agricultural applications. Students will delve into essential topics such as multimodal data integration, where diverse types of data—ranging from sensor readings to satellite imagery, computer vision, biomedical signals, sound analysis, environmental data, and signal processing—are combined to provide comprehensive insights into agricultural systems.

Through a blend of lectures, interactive discussions, case study analyses, and hands-on exercises, students will gain a thorough understanding of the digital transformation in agriculture.

This interdisciplinary course is designed to equip students with the expertise needed to tackle the complex challenges of agricultural digitization, fostering their ability to make data-driven decisions and lead responsible innovations within the field of computer science.

Emphasis will also be placed on the ethical considerations surrounding the deployment of digital technologies in agriculture, ensuring that students are prepared to contribute to sustainable and equitable advancements in this critical sector.