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An Overview of AI Technologies in African Agriculture

As demonstrated in our previous posts, agriculture plays a crucial role in the economic sector of Africa. Over 60% of sub-Saharan Africans are smallholder farmers and nearly a quarter of Africa’s GDP comes from agriculture. While agriculture already serves a great purpose in the region, there is vast room for improvement and modernization. One analysis suggests that Africa could be producing two to three more times cereals and grains than they are currently. This same prediction can also be made for horticulture crops and livestock production. An increase in production would naturally lead to stronger food security, greater household incomes, and more resilient farms, among other benefits. Artificial Intelligence (AI) could play an important role in this increased production goal.

AI is an emerging technology across various industries. In American everyday life, AI is employed to provide personalized advertisements and web search results, self-driving cars, smart thermometers, stronger cybersecurity systems. AI has also been used to fight the “fake news” epidemic by recognizing sensational or alarming words and determining credible online sources. In the healthcare industry, AI is used to analyze large sets of data to track patterns, ultimately leading to new discoveries and improved diagnostics.


AI also has great potential to advance agriculture. Technological solutions based in AI have allowed farmers to increase yield with less input, improve output quality, and allow for faster go-to market strategies. Also, this technology can minimize the use of fertilizers, pesticides, and irrigation which would improve the health of humans and the environment. Globally, farmers used 75 million AI devices in 2020 and by 2050 the average farm will collect 4.1 million data points per day. AI can specially be used in various agricultural facets like crop yield, irrigation, soil content sensing, crop-monitoring, weeding, and crop establishment.


In Africa, the effects of climate change warrant the need for more advanced technology. 50% of the droughts caused by anthropogenic climate change between 2001 and 2011 took place in Africa. Artificial Intelligence can help improve the accuracy of drought predictions and alleviate the impact of global warming on African farming communities. Small-scale rural farmers rely heavily on subsistence farming; thus, it is imperative that their crops are able to respond resiliently to drought and unpredictable weather events. AI has the potential to fulfill these needs.


The most well-known use of AI technology in agriculture is precision agriculture. This AI technology is used to detect pests and disease and nutrition in plants. The system can identify and target weeds, determine the herbicide type and amount needed, and apply it within the correct zone. This highly efficient process prevents overuse of pesticides so less toxins find their way into the food humans ultimately consume. In Africa, precision agriculture is seldom implemented, but there is work being done to increase capacity. Just recently, in December 2020, the African Conference of Precision Agriculture connected relevant stakeholders to hasten the capacity-building of precision agriculture in Africa.


Many methods of AI have already been implemented in Africa. For example, in Kenya some farmers are using near-infrared cameras mounted on drones to identify pests and diseases, among other things. Also in Kenya, a beta-stage AI tool called Nuru, can be used on Android devices with or without internet. This technology diagnoses crop diseases, specifically mite and viral diseases in cassava and armyworm infections in maize. 28,000 cassava farmers across Kenya are already benefiting from this tool.


Artificial Intelligence has great potential to make African farmers more resilient to the inevitable agricultural threats of the climate crisis. It also has potential to generally increase crop yields and use materials more efficiently and sustainably. In our next blog post, we’ll dive deeper into how AI can be used for farm management practices.

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