Machine Learning Challenges and Opportunities in the African Agricultural Sector -- A General Perspective

Ly, Racine

doi:10.48550/arxiv.2107.05101

Cited by 1 publication

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References 24 publications

(25 reference statements)

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“…Many regions lack reliable internet connectivity and face challenges in accessing advanced agricultural machinery. Limited infrastructure poses barriers to the deployment of AI-driven technologies, especially in remote and rural areas where a significant portion of agricultural activities occurs (Ly, 2021;Ozor, et al, 2023).…”

Section: Comparative Analysismentioning

confidence: 99%

AI in agriculture: A comparative review of developments in the USA and Africa

Olabimpe Banke Akintuyi

2024

Open Access Res. J. Sci. Technol.

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This comparative review explores the advancements and applications of Artificial Intelligence (AI) in agriculture, focusing on the developments in the United States (USA) and Africa. The integration of AI technologies in agriculture has witnessed significant progress globally, addressing challenges and transforming traditional farming practices. In the USA, precision agriculture and smart farming techniques driven by AI have become integral components of modern agricultural systems. These innovations include autonomous machinery, drone technology for crop monitoring, and predictive analytics for yield optimization. In contrast, the application of AI in African agriculture presents a distinct set of challenges and opportunities. The review delves into initiatives aimed at leveraging AI to enhance agricultural productivity, improve resource management, and address food security concerns in various African nations. These efforts include the deployment of AI for pest and disease detection, crop monitoring in remote areas, and the implementation of data-driven decision-making tools to support smallholder farmers. The comparative analysis sheds light on the disparities in AI adoption between the USA and Africa, emphasizing factors such as infrastructure, technological accessibility, and resource availability. Additionally, it explores collaborative efforts and partnerships that bridge the gap and contribute to the sustainable development of AI in African agriculture. As both regions navigate the complexities of implementing AI in agriculture, this review underscores the potential for technology to play a pivotal role in addressing global food challenges. The findings highlight the need for tailored approaches, policy frameworks, and international collaborations to ensure inclusive and equitable access to AI-driven innovations in agriculture, fostering a shared commitment to sustainable and technologically empowered farming practices.

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Section: Comparative Analysismentioning

confidence: 99%

AI in agriculture: A comparative review of developments in the USA and Africa

Olabimpe Banke Akintuyi

2024

Open Access Res. J. Sci. Technol.

View full text Add to dashboard Cite

show abstract

Machine Learning Challenges and Opportunities in the African Agricultural Sector -- A General Perspective

Cited by 1 publication

References 24 publications

AI in agriculture: A comparative review of developments in the USA and Africa

AI in agriculture: A comparative review of developments in the USA and Africa

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