It cannot be denied that the improvement of computation capacities, advancements in algorithmic techniques, and the significant increase of the available data have engendered the recent developments of Artificial Intelligence (AI) technology. Machine Learning (ML) 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 an 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 contextualize and discuss barriers to adoption of ML- and science data analysis-based solutions for African agriculture. The importance of adopting such technological advancement cannot be over-emphasized especially in the 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 missing the writing that is on the wall, like the proverbial ostrich burying its head in the sand.
Despite African lagging behind other regions in this regard, South Africa is poised to catch the AI wave and lead the African pack – as it were. There are already encouraging signs that South African agriculture is moving with the times as institutions of higher learning like the University of KwaZulu-Natal are already offering training in this space. The University of KwaZulu-Natal is offering training on drone piloting to its agricultural students in order to better prepare them for the marketplace of the future and that future is already here. Such tools at the hands of South African agricultural graduates sets them apart from your run-of-the-mill agricultural graduate. It is also encouraging to note that some provincial departments of agriculture and rural development are also offering similar training in current and in demand skills like drone piloting licensing. The Western Cape Department of Agriculture and Rural Development is a pioneer in this regard – having started the drone pilot training well before any other department caught on the need in the marketplace for such skills. South Africa is also a leader on the African continent in terms of precision agriculture, which could be construed as a precursor to the adoption of machine learning and data science exploitation in agriculture.
Despite the recognition and embrace of artificial intelligence by the South African agriculture sector, there is still room for improvement in order to catch-up with the rest of the world and be amongst the leaders in modernizing agriculture and food sector. The embrace of such sophisticated technology in the agri-food sector bodes well for the participation of the youth in agriculture because the millennial and other younger generations are already technologically savvy thus have a high affinity for technologically inclined industries and jobs. The advent of machine learning and use of data science in agriculture renders the sector more appealing to the youth and creates more quality jobs beyond just primary production and the mainstream value-addition which is factory-based. However, the successful take-off of machine learning and use of data science in South African agriculture and beyond is predicated availability of reliable and affordable energy supply. Currently, the unreliability of electricity supply in South Africa is one of the biggest stumbling blocks in the path of the agriculture sector pursuit of machine learning and data science. Most of the technologies and machinery required in the field of artificial intelligence and gathering of big-data is heavily reliant on a stable and reliable supply of power thus a whimsical supply of power – as is the case in South Africa at the moment- is a disincentive to investments in machine learning and data science infrastructure. Another prerequisite for successfully modernizing agriculture is availability of good quality, high-speed and widely accessible Internet connectivity, especially given that agriculture predominantly takes place in the rural areas that hardly have fibre infrastructure for high-speed network systems. Thus, increased Internet connectivity and reduced cost are indispensable if the envisaged advancement and modernization of the agriculture sector is to be realized in South Africa. Furthermore, the adoption of machine learning and data science are important tools in the arsenal to deal with the challenges of climate change and diminishing natural resources.
As a caveat, state-owned institutions in the agricultural space need to come to the party more than ever before to remove the identified barriers to entry and stumbling blocks by providing public goods through research and development. The institutions that come to mind include the Agricultural Research Council (ARC), the National Agricultural Marketing Council (NAMC), the Agriculture and Land Bank (Land Bank) and the Perishable Product and Export Control Board (PPECB), etc.
____________________________ _____________________ ___________________
Dr Thulasizwe Mkhabela is an independent agricultural researcher and policy analyst with extensive experience on South African and African agricultural issues. He is a director of Outcome Mappping and Senior Partner at Agriculture House: thulasizwe.mkhabela@gmail.com
