Training course on Big Data and AI in Agricultural Forecasting and Planning

Module 1: Introduction to Big Data and AI in Agriculture

Overview of Big Data, AI, and machine learning applications

Importance of data-driven agriculture in addressing food security

Global trends and case studies in agricultural forecasting

Module 2: Data Sources and Tools for Agricultural Forecasting

Weather, satellite, and geospatial data

IoT devices, drones, and farm-level sensors

Open data platforms and agricultural information systems

Module 3: AI Applications in Agricultural Forecasting

Machine learning for crop yield prediction

AI in climate and weather pattern forecasting

Pest, disease, and risk prediction models

Module 4: Data Analytics for Agricultural Planning

Using predictive analytics for resource allocation and farm planning

Scenario modeling and simulation for agricultural policy decisions

Tools and platforms for agricultural data visualization

Module 5: Challenges, Opportunities, and Future of Data-Driven Agriculture

Barriers to adoption: infrastructure, costs, and skills gaps

Ethical considerations, data privacy, and ownership

Future innovations: blockchain, robotics, and digital twins in forecasting

Capstone exercise: Designing an AI-powered forecasting and planning model for a selected crop or region