Data Analytics, Artificial Intelligence (AI) and Sustainable Agricultural Production for the International Market

DOI

10.2991/978-94-6463-930-8_9How to use a DOI?

Keywords

Data Analytics; Artificial Intelligence; Agricultural Sustainability; Precision Agriculture; International Market; Food Security; environmental impact

Abstract

The increasing global demand for food, coupled with the challenges of climate change and resource limitations, necessitates innovative approaches for the agricultural value chain. Advances in data analytics and artificial intelligence (AI) offer transformative potential to optimise agricultural practices, enhance productivity, and reduce environmental impact. This paper aims to explore how data analytics and AI technologies can be integrated into sustainable agricultural production systems to efficiently meet international market demands. It investigates the role of these technologies in improving crop yield, resource use efficiency, and market responsiveness. By bridging cutting-edge technologies with agricultural sustainability, this research addresses critical gaps in the current value chain. It offers insights to stakeholders—farmers, policymakers, and agribusinesses—on how to leverage AI-driven data analytics to support global food security and sustainable trade. The study employs a qualitative case study from an identified farmers and exporters group in Ghana. Results indicate that AI-powered data analytics significantly enhance precision agriculture through optimised input application and real-time monitoring. These technologies contribute to reduced waste, higher crop resilience, and facilitating sustainable supply chains for better alignment with international market standards. This paper uniquely integrates agriculture and international market dynamics with sustainability and technology frameworks. This approach offers a comprehensive perspective on implementing analytics and AI in agriculture on the global scale. It highlights novel methodologies and practical implications that have been underexplored in existing literature.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Richard Fosu
AU  - Nana Yaw Asabere
AU  - Ernest Winful
AU  - Frank Opuni Frimpong
AU  - Daniel Odoom
PY  - 2025
DA  - 2025/12/26
TI  - Data Analytics, Artificial Intelligence (AI) and Sustainable Agricultural Production for the International Market
BT  - Proceedings of the International Conference on Sustainable Business and Entrepreneurship (ICSBE 2025)
PB  - Atlantis Press
SP  - 105
EP  - 117
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-930-8_9
DO  - 10.2991/978-94-6463-930-8_9
ID  - Fosu2025
ER  -