AI in Agriculture & Data-Driven Farming (AgTech) Programme | Trajlon Group

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AI in Agriculture & Data-Driven Farming (AgTech) Programme

Course Type

Short Course

Category

Agriculture

Duration

5 Days

Ratings

Training Objectives

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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.

Target Audience

Farmers and commercial producers Agricultural extension officers and advisors Agribusiness managers and farm supervisors AgTech startups and innovators Government officials (Agriculture, ICT, Innovation, Planning) Development practitioners and NGOs Researchers, data scientists, and analysts in agriculture Youth and tech-enabled agripreneurs

Course Outline

Overview of AI, machine learning, and data analytics Evolution of AI in agriculture and food systems Key AI use cases across crop, livestock, and value chains Opportunities and limitations of AI in African contexts Case study: AI-driven agricultural innovations

Types of agricultural data (weather, soil, crop, livestock, market) Data collection: sensors, satellites, drones, IoT, mobile platforms Data quality, integration, and interoperability Preparing data for AI applications Practical exercise: Mapping an agricultural data ecosystem

Predictive analytics for yield forecasting AI-based pest, disease, and weed detection Computer vision for crop monitoring Variable-rate input application and optimisation Case study: AI-enabled precision cropping systems

AI-driven soil diagnostics and fertility recommendations Smart irrigation and water-use optimisation Climate forecasting and risk modelling Integrating AI with climate-smart agriculture practices Workshop: Designing an AI-supported climate advisory tool

AI applications in herd management and monitoring Disease detection, surveillance, and prediction Smart feeding, breeding, and welfare systems Traceability and livestock identification technologies Exercise: Applying AI to livestock management scenarios

Intelligent digital advisory platforms Chatbots, decision-support tools, and personalised recommendations AI for farmer segmentation and targeting Integrating AI into public and private extension systems Case study: AI-powered agricultural advisory services

AI-driven market intelligence and price forecasting Supply network optimisation and demand prediction Traceability, quality control, and fraud detection Digital finance, credit scoring, and insurance in agriculture Workshop: Designing an AI-enabled market linkage

AI-based AgTech business models Scaling AI solutions in agriculture Investment readiness and partnership development Public–private collaboration and innovation ecosystems Exercise: Developing an AI-enabled AgTech concept

Data ownership, privacy, and security Ethical considerations in AI and automation Bias, transparency, and accountability in AI systems Digital inclusion for smallholders, women, and youth Workshop: Developing a responsible AI framework for agriculture

Case studies: AI-driven crop and livestock systems Smart farming on commercial and smallholder farms AI-enabled agricultural value chains Group work: Designing an AI in Agriculture implementation plan Participant presentations and peer review Final reflection, evaluation, and certification

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.

Download Brochure!

Download this comprehensive brochure to explore the course overview, discover the key skills and knowledge you will gain, and review the detailed course outline – all conveniently accessible offline. Feel free to share this informative resource with colleagues and others who may benefit from this learning opportunity.

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Duration:5 Days
Modules:10
Traininglive classes
Group discount:Yes
Language:English

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AI in Agriculture & Data-Driven Farming (AgTech) Programme | Trajlon Group