In agriculture, AI can significantly transform the sector by equipping farmers with the necessary tools to increase productivity, employ more workers, and accelerate the continent’s economic growth.
By Michael Akuchie
In 2022, Africa’s population was estimated to be over 1.4 billion, with Nigeria retaining the title of “most populous nation” on the continent. Africa is also home to the youngest population in the world as 40% of the demographic is aged 15 and below. These statistics mean that the continent needs a sector that can adequately provide jobs to cater to its teeming population along with ensuring food security for future generations.
Despite the overwhelming interest in crude oil exports among some African countries, agriculture remains a major component of the continent’s gross domestic product (GDP). For instance, Sierra Leone accounted for the agriculture sector’s biggest contribution to Africa’s GDP with 60% in 2022. East and West African countries Ethiopia and Niger trailed with 38% and 36% respectively. Despite having a significant stake in the sector, Africa faces multiple challenges that have prevented it from reaching its potential. The World Economic Forum identified climate change, limited knowledge of technology, unfavourable government policies such as high import duties and export bans, and a scarcity of financing solutions as the four major problems the region must tackle.
To give African farmers and the industry a chance to minimise their losses from having to deal with the above challenges, modern technology has birthed an innovative solution known as artificial intelligence (AI). AI has diverse use cases in different industries such as healthcare, manufacturing, and finance. In agriculture, AI can significantly transform the sector by equipping farmers with the necessary tools to increase productivity, employ more workers, and accelerate the continent’s economic growth.
One of the many ways AI can revolutionise agriculture in Africa is through precision farming. Tech Target, an American company offering data-driven marketing services, defines precision farming as the act of utilising information technology (IT) to get the most out of their farm operations. Some of the use cases of precision farming include agricultural mapping and weather monitoring. Agricultural mapping is the practice whereby farmers use drones with cameras to develop high-res maps of their fields. Using this data, farmers can pinpoint problem areas, monitor crop growth, and measure the potential of the harvest.
Climate change has caused massive disruption to the agriculture sector. From increased droughts to changes in rainfall patterns, the impact of climate change has been widely felt. Weather monitoring can serve as a solution. Using weather data obtained from smart sensors and drones, farmers can be better informed about when it is safe to plant crops, the right amount of water to feed crops, and when to harvest. Precision farming has been introduced in Africa, with Rwanda making massive progress with the innovation. A video documentary about the nation’s investments in precision farming sheds more light on this practice. The video highlights the Gabiro Agribusiness Hub which was developed to address the nation’s food security needs, comprising an agriculture ecosystem with modem water infrastructure, advanced irrigation systems, and other forward-thinking tech.
Another AI-powered solution in agriculture is soil monitoring, which is the use of machines equipped with AI algorithms to help farmers yield a massive harvest. Farmers simply need to position sensors in the field to retrieve data that offers a closer look at soil temperature, moisture levels, nutrients levels, and sunlight intensity. The data collection is then analysed by machine learning algorithms to provide insights into the best conditions for farmers to grow crops along with how to care for the soil.
Last year, FarmERP, a smart farm management software provider, deployed its FarmGyan platform which uses AI, machine learning, and computer vision to boost cassava cultivation in Nigeria. It hopes to tackle the country’s cassava production problems such as pests and extreme weather conditions by monitoring the soil and detecting weed infestation through drone images and AI models.
Weeds and pests infestation cost Africa around half of its harvest yearly. Considering its fast-growing population, along with conflict and food insecurity in certain regions, the idea of less food is downright terrifying. This makes weed and pest control a serious issue that farmers must tackle with every available resource. While pesticides, sanitation, and better soil management are great options, AI promises to make weed and pest control easier for farmers to manage.
A great example of an AI-driven weed and pest control solution is laser weeding. This technology leverages AI, computer vision, and high-powered lasers to locate and destroys weeds without affecting nearby crops. Unlike the manual hand-pulling method or herbicides, laser weeding can get rid up to 5,000 weeds per minute and it can work at day or night. The LaserWeeder, an innovation developed by Carbon Robotics, a Seattle-based company, has been used to control weeds on fields of potatoes, onions, and garlic in the US states of California, Washington, and Idaho.
Although the above use cases of AI in agriculture indicate that there is plenty to gain from adoption, implementing these solutions are not so straightforward. For starters, Africa still has a long way to go in accelerating its infrastructure growth. In rural areas, drones and other innovations will be difficult due to the huge capital investment. Also, Africa relies heavily on smallholder farmers who are mostly not equipped with the technical know-how to operate these systems. Therefore, a major knowledge/skills gap must be filled by organising capacity-building programmes at the grassroots level. The use of AI also comes with certain ethical considerations. For instance, the use of AI systems may increase crop yield. Still, it can also cause soil degradation, a direct consequence of using too many pesticides to achieve short-term yield maximisation.
Still, an AI-driven agriculture sector is a welcome development for Africa. By helping farmers maximise their efforts to increase the harvest, the continent’s growing population can have access to more food, thereby reducing food insecurity in vulnerable areas. The sector is also a direct employer of labour, which should reduce the number of skilled youths opting to migrate to foreign countries. If governments across the region along with private investors partner with local farmers to adopt these technologies, the potential gains are far outweigh the risks.
Michael Akuchie is a tech journalist with four years of experience covering cybersecurity, AI, automotive trends, and startups. He reads human-angle stories in his spare time. He’s on X (fka Twitter) as @Michael_Akuchie & michael_akuchie on Instagram.
Cover Photo: Sai Sekendari | LinkedIn