Lecture 1 - Meet + greet

ANSCI 4040/6040 - Fall 2026

Dr. Hostens

Welcome

Meet the teacher

Headshot of Dr. Miel Hostens

Associate Prof. dr. Miel Hostens DVM

  • Associate Professor
  • College of Agricultural and Life Sciences
  • Department of Animal Science
  • Find out more at bovi-analytics.com

Meet your team partners

Meet the other experts

  • Sonam Yang (TA) - Graduate student & PhD candidate

  • Meike van Leerdam (TA) - Graduate student & PhD candidate

  • Sumit Sharma (TA) - Graduate student & PhD candidate

Digital Agriculture

What is Digital Agriculture

“Digital agriculture refers to the use of digital technologies, information, and communication tools in various aspects of agriculture to enhance productivity, efficiency, and sustainability. This field leverages advancements in technology, such as sensors, automation, data analytics, and connectivity, to collect, analyze, and apply data-driven insights to agricultural practices.

Course learning goals and philosophy

Course learning objectives (part 1)

  1. Explain the fundamentals of data science and digital agriculture including their interdisciplinary challenges.

  2. Implement the various data science technologies, precision farming techniques and digital tools used in modern agriculture.

  3. Conduct data collection, visualization, analysis, and interpretation within the context of digital agriculture.

Course learning objectives (part 2)

  1. Acquire practical skills through hands-on programming activities.

  2. Explore the applications of machine learning and artificial intelligence in agriculture.

  3. Recognize the significance of, and apply data privacy and security techniques in digital agriculture.

Course update

Course update

Topics that will be covered

  • Week 1 - Introduction to Data Science Applications in Agriculture

  • Week 2 - Version Control and Reproducible Workflows

    Week 3 - Cloud computing, API & Data Governance

  • Week 4 - Machine Learning & Artificial Intelligence

Two projects

  • Week 5 - 7 Mini-project
  • Week 8 -14 Team-project

Assignments

  • Small assignments in week 1-4

  • Poster presentation in week 7

  • Team project (TBD)

Course overview

Homepage

https://github.com/Bovi-analytics/BeastsAndBytes

  • All relevant course materials (and history)
  • Links to Teams, GitHub, Canvas, etc.
  • Let’s take a tour!

Activities: Prepare, Participate, Practice, Perform

  • Prepare: Introduce new content and prepare for lectures by completing the readings
  • Participate: Attend and actively participate in lectures and labs
  • Practice: Practice your programming skills (including AI)
  • Perform: Put together what you’ve learned to analyze real-world data

Grading

  • Weekly assignments wk 1 - 4 (20%)
  • Mini project (30%)
  • Team project (50%)

Support

  • Office hours
  • Ask (or even better help answering) questions during the course!!!
  • Teams
  • Reserve email for questions on personal matters

Announcements

  • During classes or email
  • If unclear, contact me

Diversity + inclusion

It is my intent that students from all diverse backgrounds and perspectives be well-served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that the students bring to this class be viewed as a resource, strength and benefit.

  • If you have a name that differs from those that appear in your official records, please let me know!
  • Please let me know your preferred pronouns.
  • If you feel like your performance in the class is being impacted by your experiences outside of class, please don’t hesitate to come and talk with me. I want to be a resource for you.
  • If something was said in class (by me or anyone) that made you feel uncomfortable, please talk to me about it.

Course policies

Team project

  • Most of the times we will meet in classrooms at the Morrison Hall building.

Academic integrity

To uphold the Cornell Academic Integrity Guide:

  • I will not lie, cheat, or steal in my academic endeavors;

  • I will conduct myself honorably in all my endeavors; and

  • I will act if the Code is compromised.

Farm data

  • Be aware that

    • the participating farmers are sharing sometimes sensitive and personal data with you (part of the course).

    • you are supposed to use that data for educational purposes only.

    • your professional conduct is expected.

    • you influence future participation of the collaborating farm(s).

Most importantly!

Ask if you’re not sure if something violates a policy!

Making Data Science Applications in Agriculture course a success

Five tips for success

  1. Help me understanding your needs as well.
  2. Read all the preparation material (if provided) before the lesson begins.
  3. Ask questions and actively collaborate with entire team.
  4. Perform the assignments.
  5. Don’t let a week pass by with lingering questions.