Agriculture has always been a vital part of Africa’s economy, supporting the livelihoods of millions of people. However, the sector is facing tough challenges due to climate change, limited resources, and problems with food security. This is where artificial intelligence (AI) comes in—a promising technology that could significantly change farming across the continent. AI can help farmers by making their work more efficient and improving decision-making. Yet, with this promise also comes some unforeseen challenges. Can AI really help farmers in Africa, or will it bring about new difficulties for them?
The Promise of AI in Agriculture through Quality Data
AI-powered farm management systems bring together data from sensors, drones, and other IoT devices to create standardized, high-quality datasets. These systems improve model training and generate actionable insights by centralizing this information. For example, AI can track crop health in real time using drones and satellites with multispectral sensors. Combining historical and real-time data helps refine best practices and ensures the quality of incoming information.
That said, AI models need large volumes of reliable data to work effectively. In agriculture, data collection isn’t always consistent due to differences in farm management practices, leading to gaps or inaccuracies. These inconsistencies can make AI models less reliable, and if farmers rely on flawed predictions, it could result in financial losses and erode trust in the technology.
This is why there are companies in Africa tackling this challenge by finding ways to collect high-quality data from farmers in the field. For instance, Tumo Kgabeng’s Agritech Solution in Botswana introduced drone technology to track pests, diseases, and soil quality, to provide actionable insights for farmers. Aerobotics in South Africa also developed AI-driven platforms utilising drone and satellite data to monitor crop health by detecting pest infestations or diseases, and optimising yields. Perennial, also in South Africa, applies AI for digital soil mapping, improving soil carbon management on the farm and supporting sustainable farming. By providing real-time, data-driven insights, these systems help farmers adjust plans on the fly, mitigating the risks of occasional inaccuracies.
Addressing Bias in AI Recommendations for Diverse Farms
Even with improvements in data collection, ensuring that AI models are fair and applicable across different farming contexts remains a crucial consideration. AI can help farmers make more informed decisions about planting, fertilising, and harvesting. These systems gather data from a wide range of environments, farm sizes and diverse soil conditions. By analysing data from various sources, and insights obtained from the localised data collected in their farms, farmers can reap increased efficiency and yield.
Nonetheless, AI systems trained on non-representative data can produce biased outcomes, leading to suboptimal or harmful farming practices recommendations. For instance, a model trained primarily on data from large-scale farms may not perform well for smallholder farms.
Some AI solutions are specifically tailored for small-scale farmers. For example, IBM’s Watson Decision Platform was deployed in Kenya to offer smallholder farmers weather forecasts and planting advice via SMS, aiding in informed decision-making. ThriveAgric in Nigeria utilises AI to offer farm-level insights, helping farmers improve productivity and manage resources efficiently. TomorrowNow also delivers farm-level insights to farmers to optimise their practices. By preferring simple solutions such as weather forecasting on a local level, or delivering farm-level insights, AI tools can reduce bias in the training data leading to more accurate insights. Farmers may also use their understanding of the local conditions in conjunction with advice from AI to come to better judgments and conclusions.
The Importance of Adapting AI to Local Agricultural Conditions
The need to cater to diverse agricultural conditions highlights the importance of adapting AI solutions to specific local contexts. Agricultural conditions vary widely across regions due to differences in climate, soil types, and farming practices. Developing AI models that can generalise across these diverse conditions is still a significant challenge.
Unsurprisingly, AI offers potential solutions to these challenges as well. AI-driven crop breeding and genetics programs can create varieties suited to local environments, addressing regional challenges. Organisations such as Agriscope Africa Limited (East African Seed Co. Ltd.) provide seeds, farm chemicals and equipment to smallholder farms in East and Southern Africa. Using AI, they can design hybrids faster and more efficiently.
Some companies use localised sensor data and weather information to tailor recommendations to specific conditions. Farmers’ Friend in Kenya develops AI-powered mobile applications providing farmers with real-time weather updates, pest and disease alerts, and personalised agronomic advice. Other companies focus on soil health and fertilisation such as: AgroAI in Tanzania which utilises AI- empowered robots to analyse soil health, perform smart fertilisation and automated irrigation, improving yields and soil sustainability; AgriCarbon in South Africa utilises AI and machine learning to automate soil carbon measurement, enhancing regenerative agriculture practices: and, Downforce Technologies operating in South Africa developed AI-powered soil carbon tracking tools, providing insights into the long-term effects of agricultural practices on soil health.
This localised data can assist AI models in curating solutions that are applicable in diverse regional setups.
Addressing the Uncertain Return on Investment in AI for Agriculture
Despite the potential benefits, farmers often consider the financial viability of adopting AI technologies. The benefits of AI adoption in agriculture may not be immediately apparent, leading to uncertainty about the return on investment (ROI). This uncertainty can deter farmers from investing in AI technologies. However, AI models can also assist in minimizing risk for smallholder farmers.
Some AI models can help farmers to predict crop yields accurately. This information aids farmers in planning and resource allocation, ensuring better market readiness and financial planning. It also assists with better risk assessment and investment decisions, thereby reducing uncertainty.
For example, AgroCenta in Ghana offers rural-based farmers market access to their produce, as well as access to financial services like micro-lending, mobile payments, insurance, savings and pensions.
Apollo Agriculture in Kenya uses satellite imagery, machine learning and AI to determine the amount of loans to offer to a farmer. They also provide advisory services on how to grow better crops based on satellite data, soil data, farmer behaviour, and crop yield estimates. Services such as yield forecasting, training & advisory services, loans, insurance, market intelligence and market linkages reduce the amount of uncertainty involved with AI tools and their ROI.
Optimising Resource Use and Potential Risks of Over-Application with AI
Beyond financial considerations, the impact of AI on resource management is a key aspect of its adoption in agriculture. AI tools can also help farms manage resources better. Advanced plant and pest analytics can ensure that fertiliser and pesticide inputs are applied optimally, thereby reducing the consumption of agricultural pesticides, saving costs, and reducing environmental pollution. AI also assists in irrigation management by analysing soil moisture and weather forecasts to optimise irrigation schedules, conserving resources and reducing costs.
However, while AI-driven precision agriculture aims to optimize resource use, inaccurate or biased recommendations may lead to the overapplication of farm inputs harming the environment. Plantix is on the spot for subverting its original goal of helping smallholder farms in India reduce pesticide use by advocating for alternative pest control methods, to connecting the farmers directly to pesticide suppliers, which raises concern about increasing pesticide dependency among small-scale farmers.
On the other hand, other companies provide information on the long-term effects of farming practices on the farm. Downforce Technologies in South Africa offers AI-powered soil carbon tracking tools, providing insights into the long-term effects of agricultural practices on soil health. Other success stories include AgroSmart in Latin America, which use an application to produce farm-specific rainfall forecasts, advice on the best time to apply pesticides, and irrigation management. Similarly, PlantVillage in Kenya employs AI to assist farmers in identifying pests and diseases and provides precise recommendations for crop management to offer farmers more predictability. Therefore, using AI can ensure that inputs are applied optimally, preserving natural resources and reducing waste.
Balancing AI with the Preservation of Traditional Agricultural Knowledge
Finally, the integration of AI in agriculture raises questions about the preservation of traditional farming knowledge, especially in Africa. These indigenous farming practices have been honed over generations and are adapted to local conditions.
On the other hand, crop monitoring and soil health monitoring systems can be designed to incorporate local traditional practices and data, preserving valuable knowledge while enhancing it with modern analytics. Integrating expert local insights into AI recommendations can create a synergy between tradition and technology. Farmers would then use the data to make better-informed decisions, leading to greater resiliency and yield improvements.
For example, the Darli AI-driven chatbot assists small farmers, particularly in Africa, by offering solutions to common agricultural problems in local languages via WhatsApp. It also integrates local data to make the information more accessible to African farmers.
AI is already reshaping agriculture in Africa, helping farmers work smarter and use their resources better. It won’t solve every challenge overnight. Data gaps, costs, and biases are real hurdles, but it’s a tool with immense potential. The real impact will come from how well it’s adapted to local farming practices, not just as a high-tech solution but as something practical and accessible. At Lanfrica, we link agricultural domain resources to help further the innovations happening in the industry to help farmers and organisations grow, adapt, and build a more resilient future for agriculture.