With the global population expected to exceed 9 billion by 2050, food security is one of the most important objectives of our time.
The agricultural economy employs 65-70 per cent of Africa’s labour force and typically accounts for 30-40 per cent of GDP according to the World Bank. With the population in Africa estimated to reach about 2.6 billion by 2050, it is now important that agriculture and food systems be reviewed in order to find innovative approaches at improving food production and utilisation to enhance food security.
Being a high-priority sector for the African economy, agriculture, broadly comprising farming and forestry, livestock (milk, eggs and meat) and fisheries, is on the verge of massive transformation with a greater focus on technology integration. Considering the spectrum of the sector, agriculture is still mired with challenges spread across the value chain and needs better optimisation of operations.
- Technology is becoming a useful tool in answering some of the African agriculture challenges.
- Artificial Intelligence stands out as one of the emerging technologies with great potential to transform the agricultural sector and provide sustainable solutions to food security in Africa.
- Artificial intelligence, including robotics, sensors, and soil sampling, is being used by farmers to gather data based on farm management systems for better processing and analysis.
Artificial Intelligence within the farming sector may be the acme of a paradigm change in how farming is now done. With a key focus on digitizing the agriculture sector, the adoption of AI has become imperative towards improving the productivity of farmers. Agricultural robotics, soil and crop monitoring and predictive analysis are increasingly becoming significant in unlocking the full agriculture potential.
The global AI valuation in the agriculture market was at US$671.6 million in 2019 and approximated to reach about US$11,200.1 million in 2030, signifying a CAGR of 30.5 per cent during the forecast period (2020-2030).
Artificial intelligence, including robotics, sensors, and soil sampling, is being used by farmers to gather data based on farm management systems for better processing and analysis.AI finds many applications in agriculture and some are listed below:
- Prediction of crop yields– tools to help farmers make ideal decisions in crop yield forecasting and improve smart farming practices that lead to higher yields;
- Prediction of soil management properties– tools for understanding soil conditions and how to boost its performance to support productivity;
- Farm management systems– tools for precision agriculture to detect and perform farm management operations such as planting, irrigation, pollination, weeding, fertilizer application, harvesting, etc.;
- Pest and disease detection– early detection of pests and diseases in the farm and eventual prevention or control;
- Smart mechanization– tools to reduce drudgery in agriculture and minimize inputs, highly autonomous and intelligent machines and agribots; and
- Livestock surveillance– for monitoring illnesses, injuries, and even pregnancies.
Integrating AI in farming machines will definitely help in optimizing operations. Investments in mechanization enable farmers to expand the range of their activities and diversify their livelihoods in ways that can reduce their vulnerability to climate change.
The availability of appropriate machinery to carry out sustainable crop management practices increases productivity per unit of land. It also increases efficiency in the various production and processing operations and in agricultural inputs’ production, extraction and transport. Artificial Intelligence methods support agriculture decision-making systems, help optimise storage and transport processes, and make it possible to predict the costs incurred depending on the chosen direction of management.
Tractor-operated tillage is the single most energy-consuming operation in crop production. Operating a plough is the main reason many farmers require high horsepower and diesel-fueled tractors. Conservation agriculture is flexible enough to accommodate the socio-economic resources of smallholder farmers as well as large-scale farming operations. Minimum soil disturbance can be achieved through digging sticks, jab planters, or mechanized direct seeders specifically developed to drill the seed through a vegetative layer.
Many methods of AI in tractors have already been implemented in Africa. For example, Hello Tractor, based in Kenya and Nigeria, launched in 2014 and incorporated AI to make an affordable, ultra-low horsepower, two-wheel tractor fitted with monitoring technology. In January 2017, Hello Tractor made the strategic decision to focus more on their application of AI than on the tractors, and it proved to be an effective model.
Hello Tractor, in partnership with IBM, also did piloting advanced agricultural analytics and decision-making tool that cut across the mechanisation ecosystem. Their data sets are used for fertiliser, seed and financial companies to access real-time, unfiltered information about a farm.
As we know, agriculture faces multiple challenges like high dependence on rainfall patterns, resource intensiveness (water, inorganic fertilisers and pesticides), degradation of land and loss of soil fertility, low per hectare yield, and more. To overcome the aforesaid challenges in agriculture, this is the time to opt for precision farming using Artificial Intelligence.
Precision farming equipment along with controlled release and deep placement technologies, make it possible to match production inputs with plant needs accurately.
The AI integration is helping farmers to improve the harvest quality and accuracy by detecting plant diseases, pests and poor nutrition on farms. For instance, the sensors of AI can detect and target weeds and help farmers to decide which herbicide needs to be applied in the region. As a result, it increases the optimum yield production from the available resources while reducing the wastage of cost and resources.
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 the 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, according to KDHI Agriculture.
According to Latent View, though Artificial Intelligence provides huge opportunities for application in the agriculture sector, there is still a lack of familiarity of high-tech machine learning solutions in farms across most parts of the world. AI based robots cost a lot of money in research and development and they also need maintenance to keep them running smoothly.
The satisfaction drawn by the AI in many fields and its application as a tool is another crucial aspect to be pondered upon. The ability of agricultural equipment to think, predict and advise farmers via a variety of artificial intelligence (AI) applications presents Africa with the potential to achieve food security.