About IFAD
At the International Fund for Agricultural Development (IFAD) we invest in rural people, empowering them to increase their food security, improve the nutrition of their families and increase their incomes. We help them build resilience, expand their businesses and take charge of their own development.Activities by IFAD
Global Initiative on AI for Food Systems
Global GermanyIndiaThe Global Initiative on AI for Food Systems, is a collaborative global effort led by the International Telecommunication Union (ITU) together with the Food and Agriculture Organization (FAO), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), and other partners to harness the transformative potential of artificial intelligence (AI) and digital technologies across the entire food value chain. It aims to boost agricultural productivity, strengthen food security, and build resilient, sustainable food systems by embedding AI into areas such as precision agriculture, supply chain optimization, and real-time decision support while addressing climate and resource challenges. A central objective is to establish common frameworks, shared digital infrastructure and standards that ensure interoperability, security, and adaptability of AI applications, enabling governments, innovators and smallholder farmers, who produce a significant portion of global food but often lack connectivity and support—to benefit from AI innovation at scale. The initiative supports knowledge exchange, synergistic partnerships, and proof-of-concept deployments to demonstrate scalable, responsible, and inclusive AI solutions that contribute to global food system resilience.
Activity Type AI Tools/SolutionsTrainings/WorkshopsTechnical AssistanceResearch/Reports/AssessmentsPolicy/Regulatory GuidanceAwareness/AdvocacyNetworks/Mentorship/ExchangeThematic Portfolio Stocktake on Fisheries & Aquaculture
Global Not ApplicableSmall-scale fisheries and aquaculture are crucial to inclusive and sustainable rural transformation, contributing to better nutrition and livelihoods while conserving natural environments. IFAD-supported projects provide access to finance, value chains, and market infrastructure to poor fishing communities, enabling them to earn more from high-quality sustainably caught or farmed fish. To support stocktaking efforts related to cataloguing Fisheries & Aquaculture projects across the IFAD portfolio, AI technologies were used to analyze IFAD project documents for information on relevant activities and target groups. By designing prompts with clear definitions, taxonomies, conditions, and objectives, Large Language Models (LLMs) deployed within a scalable AI system classified the contents of IFAD project documents to determine relevance to Fisheries & Aquaculture, allowing for a more systematic, comprehensive, reliable, and accurate assessment of this thematic area across the IFAD portfolio.
Activity Type AI Tools/SolutionsResearch/Reports/AssessmentsMapping food systems national pathways and summit dialogues with AI and ML techniques
Global Not Applicable
The analysis utilizes a supervised learning approach to classify sentences within the national pathways and summit dialogues documents. This method allows for the identification of prevalent topics across various dimensions, including food system drivers, components, and outcomes. The taxonomy for classification is derived from the HLPE Food System Framework, ensuring a comprehensive and relevant analytical lens. This analysis was carried out on specific sub-regions to primarily test the potential of this technology for policy learning. The main aim was to uncover insights from publicly available national pathways for food system agenda achievement through artificial intelligence (AI) and machine learning (ML) techniques. Several key lessons were learned. First and foremost, the project underscored the importance of leveraging advanced technologies like AI and ML to dissect and understand complex programmatic documents across diverse socio-economic contexts. The use of supervised learning to classify statements within the national pathways illuminated the distinct priorities and actions of different countries, offering nuanced insights into their food system agendas. A critical takeaway was the recognition of the heterogeneity in food system priorities across the specific sub-regions analysed and countries’ income classifications. This diversity necessitates tailored approaches in policy formulation and implementation to address the unique challenges and opportunities within each context. Furthermore, the project highlighted the significant potential of AI and ML in enhancing policy analysis and decision-making processes. By applying the HLPE Food System Framework for classification, the project demonstrated how structured taxonomies could provide a coherent framework for analysing and interpreting complex data.
Lastly, the analysis revealed the challenges and limitations of data availability and quality, particularly in countries where these documents are not available. This gap underscores the need for continued efforts to promote data generation and sharing across countries to support more comprehensive and informed policy analyses.
Harvest AI Portal
Global Not ApplicableHarvest is IFAD’s internal AI portal with IFAD-specific modules for core operations. Harvest brings custom AI solutions to IFAD for the IFAD context, designed and developed with subject matter experts across the organization. Harvest modules, powered by AI and related technologies, combine advanced tools into simple, intuitive, and user-friendly interfaces that address specific workflows. Current modules include: SECAP-Scan, Mainstreaming-Scan, Youth-Scan, Quality-Scan, Insta-Points, Insta-PR, Insta-FA, Insta-Compare, File-Chat, Content-Edit, POP-Chat.
Activity Type AI Tools/SolutionsInfrastructure/Systems Development