ARTIFICIAL INTELLIGENCE IN AGRICULTURE

Prerequisites & course information

Requisites: BSE 380 , COMP SCI 220 , 300 , or graduate/professional standing Course Designation: Core GenEd - Natural Science & Wellness + Lab Breadth - Natural Science Level - Intermediate L&S Credit - L&S Liberal Arts and Science Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Apply advanced sensing and computing techniques and modern engineering tools for intelligent agricultural system design Audience: Undergraduate 2. Apply basic machine learning models and engineering principles to solve digital agriculture problems Audience: Undergraduate 3. Identify important resources to retrieve, interpret, analyze, and critique information for use in solving problems in digital agriculture Audience: Undergraduate 4. Deliver presentations to an audience of peers discussing the development of machine learning models and interpretation of modeling results Audience: Undergraduate Learning Outcomes: 1. Apply advanced sensing and computing techniques and modern engineering tools for intelligent agricultural system design Audience: Undergraduate 2. Apply basic machine learning models and engineering principles to solve digital agriculture problems Audience: Undergraduate 3. Identify important resources to retrieve, interpret, analyze, and critique information for use in solving problems in digital agriculture Audience: Undergraduate 4. Deliver presentations to an audience of peers discussing the development of machine learning models and interpretation of modeling results Audience: Undergraduate