This unit synthesizes prior technical lessons into practical guidance for deploying AI in agricultural GIS contexts, emphasizing real-world constraints, data quality, and ethical considerations. Students will explore case studies, resource planning, team roles, and societal impacts to prepare deployment-ready projects or capstone work.
Learning Objectives
- Analyze the breast cancer and other case studies to extract transferable technical lessons and ethical considerations for AI applications in agricultural GIS
- Evaluate technical, financial, and environmental constraints (cost, connectivity, context, and technology resources) and develop mitigation strategies for field deployment
- Apply data management best practices—including quantity/quality assessment, annotation standards, and bias mitigation—and select modeling approaches appropriate for deployment scenarios
- Design a comprehensive project scope and team plan that defines roles, job types, budget estimates, and a societal impact assessment to support responsible AI adoption in agricultural settings
Curriculum
- 1 Section
- 15 Lessons
- 30 hours
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- Unit 4 — AI Deployment, Ethics, and Workforce15
- 1.1Case Study: Breast Cancer Example20 mins
- 1.2Project Scoping20 mins
- 1.3The Reality of Running AI: Cost, Connectivity, and Context20 mins
- 1.4Understanding the Role of Environmental Context in AI Deployment20 mins
- 1.5Technology Resources20 mins
- 1.6Data: Quantity and Quality, Annotations, Biases20 mins
- 1.7Modeling20 mins
- 1.8Final Thoughts20 mins
- 1.9References20 mins
- 1.10New Technologies20 mins
- 1.11AI Teams in the Near Future20 mins
- 1.12AI-Specific Jobs20 mins
- 1.13Societal Change20 mins
- 1.14Final Thoughts20 mins
- 1.15References20 mins
Instructor
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