The importance of adopting technological advancement cannot be over-emphasized especially in face of the challenges posed by climate change and unpredicted geopolitics, as demonstrated by the recent Russian invasion of Ukraine.
It, therefore, stands to reason that African agriculture should move with the times and adapt accordingly. Anything less than that would be tantamount to “stopping the clock to save time”.
It is now common knowledge that the improvement of computation capacities, advancements in algorithmic techniques, and the significant increase of available data have engendered the recent developments of Artificial Intelligence (AI) technology.
African agriculture remains largely under-developed and lagging behind other regions such as Europe, the Americas (both north and south) and parts of Asia in terms of yields and the exploitation of technology for precision agriculture.
Improvements in yields and adoption of sophisticated technologies such as drones and using real-time data through data science and machine learning are indispensable for African agriculture to improve its efficiencies and take its rightful place in the global agri-food space.
The major reason why African agriculture continues to lag behind is largely under-investment in agricultural research and development by African governments.
Private sector investments in agricultural research and development have been shown to only benefit those directly involved in the knowledge generation and development of technologies and thus have very limited public good value.
Machine Learning and data science are the two branches of AI that have shown strong capacities in mimicking characteristics attributed to human intelligence, such as vision, speech, problem-solving, and forecasting.
However, as previous technological revolutions have shown, their most significant impacts could be mostly expected on sectors of the economy that were not traditional users of that technology.
Agriculture or agri-food, as some prefer to view it, has become a sophisticated science because of the need to continue producing sufficient food, fuel and fibre sustainably with ever-decreasing natural resources and in the face of weather vagaries brought about by climate change.
The agricultural sector is vital for African economies, thus, improving yields, mitigating losses, and effective management of natural resources are crucial in a climate change era.
Machine Learning and data science are technology with added value in making predictions, hence the potential to reduce uncertainties and risk across sectors, in this case, the agricultural sector which is riddled with uncertainties and risk often beyond the control of the farmer.
The purpose of this think piece is to contextualise and discuss barriers to adoption of Machine Learning and science data analysis-based solutions for African agriculture.
The modernisation of agriculture the world-over presents both challenges and opportunities for the sector in Africa, and it can be argued that there more opportunities than challenges provided the necessary preconditions prevail.
The opportunities presented by AI and its derivatives such as Machine Learning and data science are predicated on the existence of good telecommunications network infrastructure such as broadband, affordable and accessible data and reliable power (read electricity) supply.
Both accessible data and reliable electricity supply remain the Achilles’ tendon for African nations, including those that are considered more developed such as South Africa, Kenya, Morocco, etc.
The opportunities presented by AI technological advancement to African agriculture are vast and have spill-over effects into tertiary economic sectors such as information technology, programming and scenario planning and analysis.
Thus, AI has the potential to create jobs, wealth and economic development beyond the farm gate. This is particularly important given that the majority of the African population is youthful and naturally inclined towards such professions more than primary agriculture.
In a way, Machine Learning can put a youthful and sophisticated spin to agriculture thus rendering it appealing to millennials and subsequent generations while ensuring food security and economic development through the agriculture value chain.
In summary, investments and incentives to stimulate the private sector for machine learning solutions’ creation for the agricultural sector, increased the focus on education and research, modernisation of data-gathering facilities, improvements on data management skills, ethical considerations in Machine Learning use, and privacy law frameworks, are critical components of national Artificial Intelligence (AI) strategies that most African countries do not yet have.
Given Africa’s potential to get the most out of the Machine Learning revolution, deliberate steps must be taken to fulfill this potential. Another important policy issue that will support the successful take-off of Machine Learning and, broadly, data science applications to African agriculture is the creation of an appropriate intellectual property (IP) protection policy environment in Africa to shore up investor confidence in the space.
Private sector players which are vital partners in ensuring the burgeoning of the fledging development of the AI phenomenon require an assurance that their IP will be protected in order to recoup their investment and have good returns on their investment.
Finally, it is important that an annual and Africa-wide AI Forum should be created as a place of expression and knowledge sharing between experts, researchers, policymakers, and deciders to better coordinate Machine Learning development at the continental level.
Dr. Thulasizwe Mkhabela is an independent agricultural researcher and policy analyst with extensive experience on South African and African agricultural issues.
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