Machine Learning Challenges and Opportunities in the African
Agricultural Sector -- A General Perspective
- URL: http://arxiv.org/abs/2107.05101v1
- Date: Sun, 11 Jul 2021 17:48:23 GMT
- Title: Machine Learning Challenges and Opportunities in the African
Agricultural Sector -- A General Perspective
- Authors: Racine Ly
- Abstract summary: The agricultural sector is vital for African economies.
improving yields, mitigating losses, and effective management of natural resources are crucial in a climate change era.
The purpose of this paper is to contextualize and discuss barriers to ML-based solutions for African agriculture.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: The improvement of computers' capacities, advancements in algorithmic
techniques, and the significant increase of available data have enabled the
recent developments of Artificial Intelligence (AI) technology. One of its
branches, called Machine Learning (ML), has shown strong capacities in
mimicking characteristics attributed to human intelligence, such as vision,
speech, and problem-solving. However, as previous technological revolutions
suggest, their most significant impacts could be mostly expected on other
sectors that were not traditional users of that technology. The agricultural
sector is vital for African economies; improving yields, mitigating losses, and
effective management of natural resources are crucial in a climate change era.
Machine Learning is a technology with an added value in making predictions,
hence the potential to reduce uncertainties and risk across sectors, in this
case, the agricultural sector. The purpose of this paper is to contextualize
and discuss barriers to ML-based solutions for African agriculture. In the
second section, we provided an overview of ML technology from a historical and
technical perspective and its main driving force. In the third section, we
provided a brief review of the current use of ML in agriculture. Finally, in
section 4, we discuss ML growing interest in Africa and the potential barriers
to creating and using ML-based solutions in the agricultural sector.
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