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ArticleMaking food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools 2024
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No results found.To enhance the resilience of food systems to food safety risks, it is vitally important for national authorities and international organizations to be able to identify early signals of emerging food safety risks and to provide early warning in a timely manner. This review provides an overview of existing and experimental applications of artificial intelligence (AI), big data, and internet of things tools and methods as part of early warning and emerging risk identification in the food safety domain. There is an ongoing rapid development of systems fed by numerous, real-time, and diverse data with the aim of early warning and identification of emerging food safety risks. The suitability of big data and AI to support such systems is illustrated by two cases in which climate change drives the emergence of risks, namely, harmful algal blooms affecting seafood and fungal growth and mycotoxin formation in crops. Automation and machine learning are crucial for the development of future real-time food safety risk early warning systems. Although these developments and tools increase the feasibility and effectiveness of prospective early warning and emerging risk identification, their implementation may prove challenging, particularly for low- and middle-income countries due to low connectivity and data availability. It is advocated to overcome these challenges by improving the capability and capacity of national authorities, as well as by enhancing their collaboration with the private sector and international organizations. -
BookletExploring the application of Artificial Intelligence for triggering drought anticipatory action: A Timor-Leste case study
Technical Working paper
2024Also available in:
No results found.This research describes the process of developing an agricultural drought-triggering methodology for anticipatory action (AA) within the context of Timor-Leste, an Indo-Pacific nation grappling with limited observation data. Drought is a severe and recurring natural hazard in Timor-Leste, significantly impacting livelihoods and exacerbating food insecurity due to the compounding effects of the climate crisis. This study provides a comprehensive understanding of the methodology’s development, highlighting the collaborative establishment of an AA protocol with the government and the humanitarian community, spearheaded by the Food and Agriculture Organization of the United Nations and the Government of Timor-Leste. Overall, this study aims to facilitate a transition towards a preemptive approach for disaster risk management and highlight the advances of the introduction of Artificial Intelligence (AI) moving forward. -
Book (stand-alone)Using artificial intelligence to assess FAO’s knowledge base on the technology accelerator 2023
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No results found.Harnessing science, technology and innovation (STI) is key to meeting the aspirations of efficient, inclusive, resilient and sustainable agrifood systems and leveraging emerging opportunities to achieve the Sustainable Development Goals (SDGs). The FAO Strategic Framework 2022–2031 identifies STI as having enormous transformative potential and underlines the potential of emerging technologies. It also recognizes that STI can present substantial risks, such as reinforcing inequality and market concentration, or contributing to the degradation of natural resources. As one of four accelerators identified by the FAO Strategic Framework 2022–2031, technology is expected to “accelerate impact while minimizing trade-offs”. This report examines the technology accelerator trends across publicly available FAO knowledge reports, technical guidance and convening summaries. Leveraging AI-assisted classification of nearly 40 000 documents, this report offers a bird’s-eye perspective of six types of technology – digital technologies, biotechnologies, mechanization, irrigation technologies, renewable energy technologies and food processing technologies – as well as high-level trends for outcomes and social and demographic details about the communities using these technologies.
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