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Abstract
“To till and to keep it,” as used in Genesis 2:15, presupposes a responsible use of the earth’s resources for human sustenance and the preservation of the planet. The biblical text, through hermeneutical reimagining, could potentially support the creation and deployment of autonomous systems, such as artificial intelligence, which could improve upon existing traditional methods of agricultural production. Recent developments in Artificial Intelligence (AI) offer the opportunity to scale farm production and achieve food security in Africa while maintaining balance in the agro-ecosystem. This study aims to interpret Genesis 2:15 in favour of leveraging AI for sustainable and improved agricultural production and food security in Africa. The work adopts the reading of recovery approach and argues that the interpretive implications of the biblical text support the responsible and ethical use of autonomous systems in solving agricultural-related problems. The study finds that the interpretation of Genesis 2:15 aligns with the use of AI as a multifaceted tool at human disposal in scaling agro production in contemporary times. It also finds that innovation in agricultural systems through a responsible use of AI could potentially remove African nations from the list of hungry nations of the world. The paper concludes that although the use of AI raises critical theological, moral, and ethical questions, when viewed through the lens of the Genesis 2:15 mandate, it makes sense to include AI as one of the many tool’s humans have created in fulfilment of the assignments in Genesis 2:15.
References
Adamo, D.T. (2015). The Task and Distinctiveness of African Biblical Hermeneutics. Old Testament Essays OTE 28(1), 31-52. http://dx.doi.org/10.17159/2312-3621/2015/v28n1a4.
AFDB (2022). Feed Africa. African Development Bank Group. Retrieved from https://www.afdb.org
Ahiamadu, A.E. (2020). Redefining stewardship in Nigeria: a theological, Ethical and critical analysis of oil resources management and land use. Pearl Publishers International Ltd.
Bang, K. (2021). A reading of recovery for an ethical interpretation of Joshua's harem passages. Korean journal of Old Testament studies. 27(1), 61-96. DOI: 10.24333/jkots.2021.27.1.61
Brown, W.P. (2015). Sacred sense: Discovering the wonders of God’s word and world. W.B. Eerdmans Publishing Company
Cieniawska, B. & Pentos, K. (2021). Average degree of coverage and coverage unevenness coefficient as parameters for spraying quality assessment. Agriculture 11(183), 39-54. DOI: 10.3390/agriculture1102015
Cook, P., & O’Neill, F. (2020). Artificial intelligence in agribusiness is growing in emerging market. EMCompass.
Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How Artificial Intelligence Will Change the Future of Marketing Journal of the academy of marketing science, 48(1), 24–42.
Davis, F.E. (2009). Scripture, culture, and agriculture: an agrarian reading of the Bible. New York: Cambridge University Press.
DeFries, R., & Rosenzweig, C. (2010). Toward a whole-landscape approach for sustainable land use in the tropics. PNAS, 107(46), 19627–19632. https://doi.org/10.1073/pnas.1011163107
Dumbrell, W. (1988). The faith of Israel: Its expression in the books of the Old Testament. Inter-Varsity Press.
Eli-Chukwu, N. C. (2019). Application of artificial intelligence in agriculture: A review. Engineering, technology & applied science research, 9(4), 4377–4383.
Encyclopaedia Britannica (N.D). Jethro Tull. Retrieved from, https://www.britannica.com/technology/grain-drill
FAO, AUC, ECA, & WFP (2023). Africa: Regional Overview of Food Security and Nutrition 2023: Statistics and Trends. Accra, FAO. https://doi.org/10.4060/cc873en
Galal, S. (2025). Contribution of agriculture, forestry, and fishing sector to the Gross Domestic Product (GDP) in Africa as of 2023, by country. Statista. Retrieved from, https://www.statista.com/statistics/1265139/agriculture-as-a-share-of-gdp-in -afirica-by-country/
Gercia-Martinez, H. et al. (2020). Corn grain yield estimation from indices, canopy cover, plant density and a neural network using multispectral and RGB images acquired with uncrewed aerial vehicles. Agriculture 10 (277), 199–222. DOI: 10.3390/agriculture10070277
Gerhards, R., & Christensen, S. (2003). Real-time weed detection, decision making and patch spraying in maize, sugarbeet, winter wheat and winter barley. Weed research, 43(6), 385–392.
High-Level Expert Group on Artificial Intelligence. (2019). A definition of AI: Main capabilities and scientific disciplines. European Commission. Available at: https://ec.europa.eu/newsroom/dae/document.cfm?doc_id=56341
Intergovernmental Panel on Climate Change (IPCC) (2022). Special report: Special report on climate change and land. Retrieved from https://www.ipcc.ch/srccl/chapter/chapter-5/#:~:text=Changing%20dietsview
International Food Policy Research Institute (IFPRI) (2021). Global food policy report 2021: food system transformation and the impact of COVID-19 on African economies. Retrieved from, https://www.ifpri.org/blog/global-food-policy-report-202/
Jaiyesimi, F. (2024). AI is seen as improving agricultural value chain efficiency, tackling food insecurity. Business Day. Retrieved from https://businessday.ng/agriculture/article/ai-seen-improving-agric-value-chains-efficiency-tackling-food-insecurity/
Kakani, V., Nguyen, V. H., Kumar, B. P., Kim, H. & Pasupuleti, V. R. (2020). A critical review on computer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research, 2, 100033
Kavusa, J.K. (2019). Ecological hermeneutics and the interpretation of biblical texts yesterday, today and onward: Critical reflection and assessment. Old Testament essays OTE, 32(1), 229-255.
KJV: King James version of the Holy Bible.
Kujawa, S. & Niedbala, G. (2021). Artificial neural networks in agriculture. In Kujawa, S. & Niedbala, G. (Eds.) Agriculture: Artificial neural networks in agriculture, (pp. 1-6). MDIP. DOI: 10.3390/agriculture11060497
Laikos, K.G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674
Liu, S. Y. (2020). Artificial intelligence (AI) in agriculture. IT Professional, 22(3), 14–15.
Marwala, T. (2024). Artificial intelligence can transform global food security and climate action. UN University. Retrieved from https://unu.edu/article/artificial-intelligence-can-transform-global-food-security-and-climate-action
Mazoyer, M. & Roudart, L. (2006). A history of world agriculture: from the Neolithic Age to the current crisis. New York: Monthly Review Press.
O’Brien, P., & Keyder, C. (2012). Economic growth in Britain and France 1780-1914: Two paths to the twentieth century (Routledge Revivals). Routledge.
Paarlberg, D. & Paarlberg, P. (2000). The agricultural revolution of the 20th century. Iowa: Iowa State University Press.
Pretty, J., & Bharucha, Z. P. (2014). Sustainable intensification in agricultural systems. Annals of Botany, 114(8), 1571-1596. https://doi.org/10.1093/aob/mcu205
RSV: Revised Standard Version of the Holy Bible.
Ryan, M., Isakhanyan, G., & Tekinerdogan, B. (2023). An interdisciplinary approach to artificial intelligence in agriculture. NJAS: Impact in Agriculture and Life Sciences, 95(1), 2168568. DOI: 10.1080/27685241.2023.2168568.
Schoeman, M. (2024). How AI Can Inclusively Transform Agri-Food Systems in Africa. Brookings Institution. Retrieved from https://www.brookings.edu/articles/how-ai-can-inclusively-transform-agri-food-systems-in-africa/
Tarasiewicz, J. (2023). 12 examples of the best agricultural artificial intelligence in action. Retrieved from https://www.atltranslate.com/aiRetty/blog/agriculture-artificial-intelligence-farming-12-examples?
Tobal, A. (2014). Weed identification using an evolutionary artificial intelligence algorithm. Journal of Computer Science, 10(8), 1355–1361.
Waltke, B. K. (2001). Genesis: A commentary. Zondervan Publishing House.
Wang, M., Wang, J., & Chen, L. (2020). Mapping paddy rice using a weakly supervised long short-term memory network with time series Sentinel optical and SAR images. Agriculture 10 (483), 199–222. DOI: 10.3390/agriculture10100483.
White, L. (1967). The historical root of our ecological crisis. Science, 155. 1203–1207. https://doi.org/10.1126/science.155.3767.1203.
World Food Programme (WFP) (2023). Annual report: Keeping peace with soaring humanitarian challenges amid shrinking resources. Retrieved from, https://publications.wfp.org/2023/en/annual-report/
Yang, C. C., Pracher, O. S., Landry, J., & Ramaswamy, S. H. (2002). Development of a neural network for weed recognition in cornfields. Transactions of the ASAE, 45(3), 859.
Wenham, J.G. Word Biblical Commentary: Genesis 1-15, Vol. 1. Eds.: Hubbard, D. A., Watts, J. D. W., et al. Waco, (Texas: Word Books Publishers, 1987), p. 26.
Wasike, A.N. (1999). Genesis 1-2 and some elements of diversion in African tradition: Implication for today (pp. 175-180). In Getui, M. (Ed.) Interpreting the Old Testament in Africa. Peter Lang.
Ademiluka, O. S. (2008). Genesis 1-3 in an African setting. Nathadex Publisher.
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