Foundations of AI for Agriculture

This unit introduces the historical development, core definitions, and technical foundations of artificial intelligence, with emphasis on machine learning, neural networks, and common deep learning models. It situates AI within the 21st-century economy and prepares learners to evaluate and apply these concepts to GIS applications in agriculture in subsequent units.

Learning Objectives

• Analyze key historical milestones and paradigm shifts in AI (from early innovators through AI winters to modern resurgence) and explain their influence on contemporary agricultural GIS technologies
• Demonstrate understanding of core AI concepts by defining AI, differentiating types of machine learning, and describing how machines learn using algorithms and training data
• Evaluate neural network architectures and common deep learning models by identifying their strengths, limitations, and suitability for spatial and agricultural datasets
• Apply knowledge of AI’s economic, ethical, and societal impacts to propose responsible, data-informed approaches for integrating AI into GIS solutions for agriculture