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Agriculture Drone

Teaching 

I believe in supporting students with the skills and knowledge needed to address the current agricultural and environmental challenges using modern tools. My teaching approach integrates cutting-edge technologies, real-world applications, and collaborative opportunities to ensure students are prepared to make meaningful contributions to their careers.

What makes my teaching unique?

  1. Hands-on learning makes students use real-world data and tools, including soil analysis methods, remote sensing technologies, and machine learning applications. 

  2. Research integration with current research projects, which allows students to have the opportunity to contribute to initiatives like soil health mapping, grassland monitoring, and crop yield prediction.

  3. Interdisciplinary focus is needed to bridge disciplines, combining insights from soil science, agronomy, climate studies, and data science to equip students with a holistic understanding of agricultural systems.

  4. Collaborative opportunities when students benefit from the lab’s partnerships with industry leaders, government agencies, and academic institutions. 

1

Introduction to Digital Agriculture - AGRI 3100

An introduction to precision agriculture and innovative farming principles discussing digital agriculture from farm to fork. Students will develop a concrete understanding of the theoretical and practical knowledge of computational agriculture, data-driven modelling, and agriculture data science in modern farming systems.

2

Soil and Water Management - SOIL 4510

This course promotes critical thinking about agricultural production, limiting factors, sustainability, and environmental impacts. Topics include the capability of land for agriculture, storage, movement, and use of water; saline and alkaline soils; soil conservation, including erosion; sustainability of soil organic matter; and the effect and fate of soil amendments.

3

 Special Topics in Soil Science: Machine Learning in Agriculture -  SOIL 7250 

In this course, several techniques in machine learning will be introduced using spatial and non-spatial datasets such as tabular, vector, and raster.  Data sources may include remote sensing imageries and GIS. Understanding the reliabilities of the analytics is crucial and will be discussed during the course. 

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