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

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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