We're still actively developing this site. If you encounter any issues, please report them! - Report an issue

ARTIFICIAL INTELLIGENCE IN AGRICULTURE

BSE 405
Course Description

Provides an understanding of how cutting-edge Artificial Intelligence (AI) technologies revolutionize and optimize various aspects of the agricultural sector. Covers topics related to advanced sensors for data acquisition, data processing and visualization, and machine learning models to inform agricultural decision making. Introduces both theoretical concepts and practical insights into real-world AI implementation in agriculture.

Prerequisties

BSE 380 , COMPSCI 220 , COMPSCI 300 , or graduate/professional standing

Satisfies

This course does not satisfy any prerequisites.

Credits

Not Reported

Offered

Not Reported

Grade Point Average
3.54

3.29% from Historical

Completion Rate
100%

No change from Historical

A Rate
28.57%

-7.14% from Historical

Class Size
14

7.69% from Historical

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

Similar Courses

INTRODUCTORY DATA SCIENCE FOR THE AGRICULTURAL AND LIFE SCIENCES

BSE 380

Agricultural and life scientists need to creatively apply physical, chemical, and biological principles to address technical, business, and environmental challenges. Many of these challenges involve numerical analyses, including the interpretation of large datasets. The fundamentals of computer coding using numerical software will be taught, using real-world data science challenges from the agricultural and life sciences.

DIGITAL TECHNOLOGIES FOR ANIMAL MONITORING

ANSCI/BSE 344

Introduces key concepts of sensor technology used for livestock and companion animal monitoring and veterinary medicine. Describes applications of Artificial Intelligence (AI) systems for livestock animals and veterinary medicine, including animal monitoring, computer-aided diagnosis, and optimized farm management decisions.

PRECISION AGRICULTURAL TECHNOLOGIES

FISC 145

Precision agriculture can aid in reducing inputs for crop production. Course provides an overview of precision agriculture technologies and will cover Global Positioning Systems, Geographic Information Systems, variable rate technology, section/flow control, soil and yield mapping, and guidance systems. Economics of the different technologies will be discussed. Previous experience with precision agriculture systems is not required.

ENGINEERING PRINCIPLES OF AGRICULTURAL MACHINERY

BSE/ME 475

Engineering design principles of machines for the production, processing and handling of crops for food, fuel, bio-mass and fiber. Environmental and biological factors that influence machine design and operation. Economic and capacity analysis of machines and systems.

DATA SCIENCE FOR AGRICULTURAL AND APPLIED ECONOMICS

AAE 718

Introduction to data and data processing using both Python and R programming languages. Concepts covered include loading data, data acquisition, cleaning data, visualization/exploring data, and storing data.