Master AI and Data-Driven Farming (AgTech)
Master the strategic, technical, and applied skills to leverage Artificial Intelligence (AI), advanced analytics, and digital technologies for transforming agricultural production, enhancing decision-making, productivity, and sustainability across the value chain.
AI in Agriculture & Data-Driven Farming (AgTech) Programme
Master the strategic, technical, and applied skills to leverage Artificial Intelligence (AI), advanced analytics, and digital technologies for transforming agricultural production, enhancing decision-making, productivity, and sustainability across the value chain.
Course Overview
The AI in Agriculture & Data-Driven Farming (AgTech) Programme equips participants with the strategic, technical, and applied skills required to use artificial intelligence (AI), advanced analytics, and digital technologies to transform agricultural production and value chains. The programme focuses on how AI can enhance decision-making, productivity, resilience, sustainability, and profitability across crop, livestock, and agribusiness systems. The course integrates AI tools, machine learning applications, precision agriculture, and digital platforms with Climate-Smart Agriculture (CSA) and market-oriented farming systems. It aligns with Agenda 2063, the SDGs, national digital agriculture strategies, AI policies, and global AgTech innovation frameworks. Participants gain hands-on exposure to AI-driven use cases such as predictive analytics, computer vision, remote sensing, and intelligent advisory systems, while also addressing ethics, data governance, and inclusion.
By the end of this training, participants will be able to:
- Explain key AI concepts and their relevance to agriculture and food systems.
- Apply data-driven and AI-supported decision-making in farming and agribusiness.
- Use AI tools for crop, livestock, soil, water, and climate management.
- Understand predictive analytics, machine learning, and computer vision applications in agriculture.
- Integrate AI into extension, advisory, and market information systems.
- Evaluate costs, benefits, and risks of AI and AgTech investments.
- Address data governance, ethics, and digital inclusion in AI-driven agriculture.
- Design AI-enabled agricultural projects and strategies.
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