Artificial Intelligence (AI) in Agriculture: Principles and Applications
AESC 4200/6200
3 hours
Artificial Intelligence (AI) in Agriculture: Principles and Applications
Course Description
Explore applications of AI in modern agriculture. Students will learn how to apply AI in the agricultural domain that include analysis of numerical data, computer vision applications, and natural language processing. Class periods will consist of lectures, coding demos, hands-on exercises, and discussions of example applications.
Additional Requirements for Graduate Students: The course projects for graduate students will include an advanced application of AI technologies.
Athena Title
Artificial Intelligence in Ag
Undergraduate Prerequisite
STAT 2000 or STAT 2000E or STAT 2100H
Graduate Prerequisite
Any STAT course
Semester Course Offered
Offered fall
Grading System
A - F (Traditional)
Student learning Outcomes
After completing this course, students will be able to understand the foundational concepts of AI.
After completing this course, students will be able to use Python programming language and its libraries to code AI projects.
After completing this course, students will be able to develop proficiency in using AI-powered tools and algorithms.
After completing this course, students will be able to learn and use different agricultural data processing approaches.
After completing this course, students will be able to understand the challenges and prospects for AI applications in agriculture.
Topical Outline
Introduction, history, and foundational concept of AI
Discussion of challenges in modern agriculture that require AI solutions
Programming and different useful libraries for AI
Data sorting, pre-processing, and visualization
Regression and classification approaches for agricultural data processing
Prediction modeling for agriculture
Statistical machine learning for numeric agricultural data
