Deep Learning Indaba 2024
2nd international joint conference of Artificial intelligence, 1-7 September 2024 | Dakar, Senegal
Big data and advanced data analytics are pivotal in fostering interoperable systems for inclusive economic growth, particularly within the agricultural sector. The integration of these technologies is essential for addressing global food security challenges exacerbated by climate change. The agricultural industry
faces unprecedented challenges due to the rapidly evolving global markets, environmental concerns, and the pressing need to meet Sustainable Development Goals (SDGs). The inherent complexity of agricultural value chains, characterized by dynamic interactions among diverse stakeholders, complicates these challenges.
However, advancements in sensor technology, data mining, and analytics, traditionally used in other industries, are now transforming farming systems. This transformation aligns with the SDG goal of Innovation and Infrastructure, fostering more efficient and sustainable agricultural value chains. The rise of "Big Data" in
agriculture, characterized by the "5Vs" (Volume, Variety, Velocity, Veracity, and Value), has enabled novel analyses, such as weather data processing, for timely decision-making. Despite data availability, inefficiencies arise due to the complex and fragmented nature of data sourcing and processing. Therefore, integrating artificial intelligence (AI) technologies, including machine learning, large language models (LLMs), and predictive analytics, is increasingly important in managing these complex systems. AI tools can process vast amounts of data, identify patterns, and provide insights that support informed decision-making and adaptive strategies. Developing enterprise architectures (EA) is crucial for integrating AI solutions within agricultural value chains. EA provides a structured approach to align people, processes, and technology, ensuring AI initiatives are strategically aligned with business objectives and data governance standards. As agriculture moves towards a more data-driven approach, the role of AI and enterprise systems in enhancing sustainability, efficiency, and resilience becomes more significant. This paper highlights the transformative potential of AI and enterprise architecture in revolutionizing agriculture, offering an enterprise agricultural platform a comprehensive solution to modern agricultural challenges in the Society 5.0 era. Future research could explore the integration of LLMs with AI-driven predictive analytics to optimize agricultural practices further, promoting sustainable and efficient agriculture.
Keywords: Enterprise Systems, Data Interoperability, Artificial Intelligence, Agricultural Analytics, Big Data, Society 5.0.
