References
Adisa OM, Botai JO, Adeola AM, Hassen A, Botai CM, Darkey D, Tesfamariam E(2019) Application of artificial neural network for predicting maize pro-duction in South Africa. Sustainability 11(4):1145. https://doi.org/10.3390/su11041145 Arakpogun EO, Elsahn Z, Olan F, Elsahn F (2021) Artificial intelligence in Africa:Challenges and opportunities. The fourth industrial revolution: Imple-mentation of artificial intelligence for growing business success. 375– 388.https://doi.org/10.1007/978-3-030- 62796-6_22 Chaterji S, DeLay N, Evans J, Mosier N, Engel B, Buckmaster D, Chandra R (2020)Artificial intelligence for digital agriculture at scale: techniques, policies, andc hallenges. https://doi.org/10.48550/arXiv.2001.09786 Ditzler L, Driessen C (2022) Automating agroecology: how to design a farming robot without a monocultural mindset? J Agric Environ Ethics 35(1):2. Foster L, Szilagyi K, Wairegi A, Oguamanam C, de Beer J (2023) Smart farming and artificial intelligence in East Africa: addressing indigeneity, plants, and gender. Smart Agric Technol 3:100132. https://doi.org/10.1016/j.atech.2022.100132 Gikunda K (2024) Harnessing artificial intelligence for sustainable agricultural development in Africa: opportunities, challenges, and impact. https://doi.org/10.48550/arXiv.2401.06171 Goralski MA, Tan TK (2020) Artificial intelligence and sustainable development.Int J Manag Educ 18(1):100330. https://doi.org/10.1016/j.ijme.2019.100330 Gorlapalli A, Kallakuri S, Sreekanth PD, Patil R, Bandumula N, Ondrasek G, Admala M, Gireesh C, Anantha MS, Parmar B (2022) Characterization and prediction of water stress using time series and artificial intelligence models.Sustainability 14(11):6690. https://doi.org/10.3390/su14116690 Gruetzemacher R, Paradice D, Lee KB (2020) Forecasting extreme labor dis-placement: a survey of AI practitioners. Technol Forecast Soc Change161:120323. https://doi.org/10.1016/j.techfore.2020.120323 Gwagwa A, Kraemer-Mbula E, Rizk N, Rutenberg I, De Beer J (2020) Artificial intelligence (AI) deployments in Africa: benefits, challenges and policy dimensions. Afr J Inf Commun 26:1– 28. https://doi.org/10.23962/10539/30361 Ikudayisi A, Calitz A, Abejide S (2022). An artificial intelligence approach to manage crop water requirements in South Africa. Online J Eng Sci. 23– 34. https://doi.org/10.31586/ojes.2022.377 Kiobia DO, Mwitta CJ, Fue KG, Schmidt JM, Riley DG, Rains GC (2023) A review of successes and impeding challenges of IoT-based insect pest detection systems for estimating agroecosystem health and productivity of cotton.Sensors 23(8):4127. https://doi.org/10.3390/s23084127 Kouadio L, Deo RC, Byrareddy V, Adamowski JF, Mushtaq S (2018) Artificial intelligence approach for the prediction of Robusta coffee yield using soil fertility properties. Comput Electron Agri 155:324– 338. https://doi.org/10.1016/j.compag.2018.10.014 Kouadio, L., Newlands, N., Davidson, A., Zhang, Y., Chipanshi, A., & Hill, H. (2018). Machine learning for crop yield prediction: Integrating climate and satellite data. Remote Sensing, 10(2), 313. Mark, S. (2019). Data colonialism in digital agriculture: Toward a decolonial AI. Global Information Society Watch Mark R (2019) Ethics of using AI and big data in agriculture: the case of a large agriculture multinational. ORBIT J 2(2):1– 27. https://doi.org/10.29297/orbit.v2i2.109 Nhemachena C, Nhamo L, Matchaya G, Nhemachena CR, Muchara B, KaruaiheST, Mpandeli S (2020) Climate change impacts on water and agriculture sectors in Southern Africa: threats and opportunities for sustainable devel-opment. Water 12(10):2673. https://doi.org/10.3390/w12102673 Omeiza D (2019) Efficient machine learning for large-scale urban land-use fore-casting in Sub- Saharan Africa. https://doi.org/10.48550/arXiv.1908.00340 Rutherford B (2017) Land governance and land deals in Africa: opportunities and challenges in advancing community rights. J Sustain Dev Law Policy 8(1):235– 258. https://doi.org/10.4314/jsdlp.v8i1.10 Sampene AK, Agyeman FO, Robert B, Wiredu J (2022). Artificial intelligence as a path way to Africa’ s transformations. Artif Intell 9(1). https://www.researchgate.net/profile/Agyemang- Sampene/publication/358440753_Artificial_Intelligence_as_a_Path_Way_to_Africa’ s_ TransformationS/links/620a060bcf7c2349ca124bb1/Artificial-Intelligence-as-a-Path- Way-to-Africas-TransformationS.pdf Songol M, Awuor F, Maake B (2021) Adoption of artificial intelligence in agriculture in the developing nations: a review. J Lang Technol EntrepAfr 12(2):208– 229. https://www.ajol.info/index.php/jolte/article/view/221709 Sparrow R, Howard M, Degeling C (2021) Managing the risks of artificial intelli-gence in agriculture. NJAS: Impact Agric Life Sci 93(1):172– 196. https://doi.org/10.1080/27685241.2021.2008777 Tzachor A, Devare M, King B, Avin S, Ó hÉigeartaigh S (2022) Responsible arti-ficial intelligence in agriculture requires systemic understanding of risks and externalities. Nat Mach Intell 4(2):104– 109. https://doi.org/10.1038/s42256-022-00440-4 Uddin M, Chowdhury A, Kabir MA (2024) Legal and ethical aspects of deploying artificial intelligence in climate-smart agriculture. AI SOC 39(1):221– 234.https://doi.org/10.1007/s00146-022-01421-2 Boko, M., et al. (2007). Africa. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the IPCC. FAO. (2021). The Impact of Disasters and Crises on Agriculture and Food Security: 2021. Rome: Food and Agriculture Organization of the United Nations. HLPE. (2020). Food Security and Nutrition: Building a Global Narrative towards 2030. High Level Panel of Experts on Food Security and Nutrition of the Committee on World Food Security. IPCC. (2022). Climate Change 2022: Impacts, Adaptation, and Vulnerability. Working Group II Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Niang, I., et al. (2014). Africa. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. IPCC Fifth Assessment Report. Porter, J. R., et al. (2014). Food security and food production systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Sultan, B., & Gaetani, M. (2016). Agricultural impacts of climate change in West Africa: A review. Climatic Change, 141(1), 49– 61. Sumberg, J., Thompson, J., & Woodhouse, P. (2020). African agriculture and AI: Debates on inclusion and relevance. Development and Change, 51(5), 1265– 1291. Thornton, P. K., Ericksen, P. J., Herrero, M., & Challinor, A. J. (2018). Climate variability and vulnerability to climate change: A review. Global Change Biology, 20(11), 3313– 3328.