Artificial intelligence did not appear overnight. Its roots go back many decades, but modern AI began to take shape as a scientific discipline in the middle of the 20th century. In 1950, British mathematician Alan Turing asked whether machines could demonstrate behaviour that could be considered intelligent. Six years later, researchers meeting at Dartmouth College in the United States formally established artificial intelligence as a field of research.
The technology went through periods of excitement and disappointment. Early computers could solve mathematical problems and play games, but computing power and available data were limited. The major transformation came during the 2010s, when powerful processors, enormous datasets and advances in machine learning made it possible for computers to recognise images, understand speech and analyse enormous amounts of information.
The arrival of generative AI and systems such as ChatGPT brought the technology into everyday life. But for farmers, perhaps the most important question is not what AI can do in an office, but what it can do on a farm.
AI IS MOVING INTO THE FARMING FIELD
Agriculture is particularly suited to AI because farming produces enormous amounts of information. Soil tests, rainfall, temperature, satellite images, machinery data, yield maps, livestock records, market information and crop observations can all become part of a much larger picture.
AI can analyse these different sources much faster than a person can. The objective is not simply to collect information, but to turn information into useful decisions.
The FAO says AI is already being explored for precision farming, predictive analytics, disease detection, drought monitoring, irrigation and more efficient use of resources.
For a crop farmer, this could mean identifying areas of a field showing stress before the problem becomes obvious from the tractor cab. Satellite or drone imagery combined with AI can help identify variations in crop growth, soil conditions, weeds or disease.
South Africa is already developing this type of technology. The CSIR, working with the Agricultural Research Council and the Department of Science, Technology and Innovation, is developing precision-agriculture systems using satellite information, sensors, drones and AI-powered analysis. These systems are designed to provide farmers with information about soil and crop health, yield prediction and input management.
WHAT COULD THIS MEAN FOR A FARMER?
Imagine starting the morning with more than just a weather forecast.
An AI-supported farm management system could bring together rainfall information, soil moisture, crop growth, historical production, satellite imagery and machinery data. Instead of treating an entire farm as though every hectare is identical, the farmer could receive information showing where conditions differ.
One part of a field may require attention while another does not.
This has potentially important implications for fertiliser, water, pesticides, fuel and labour. AI can assist with identifying where inputs may be needed and where they may not be. Research reviewed through the FAO AGRIS system identifies applications including crop monitoring, irrigation management, weed and pest control, yield prediction and smart spraying.
The same principle applies to livestock.
AI systems can increasingly analyse animal behaviour, movement and other data to help identify changes that may require attention. The Agricultural Research Council's current strategic plan identifies AI applications in livestock health and behaviour, disease detection, precision spraying, yield prediction and resource-efficient farming.
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AI WILL NOT REMOVE THE FARMER FROM THE FARM
This is perhaps the most important point.
AI should not be seen as a replacement for agricultural knowledge, experience and observation. A computer can analyse data, but it does not walk across a field, feel the soil, inspect a crop after a storm or understand the history of a particular farm in the same way as an experienced farmer.
The future is therefore more likely to be about farmer plus technology, rather than farmer versus technology.
FAO's work on AI in agriculture specifically stresses that technology needs to be designed around farmers' real conditions, including local soils, farming systems, connectivity and digital skills.
For South Africa, this is particularly important because farmers operate under very different conditions. A technology developed for a large irrigated farm in another country cannot simply be assumed to work perfectly on every South African farm.
THE DATA BELONGING TO THE FARMER
There is another issue farmers need to understand: data.
As farming becomes more digital, valuable information about soil, yields, production methods, machinery, livestock and farm operations is being collected. Farmers need to know who owns that information, who can access it, how it is stored and how it may be used.
The OECD has highlighted data governance as an important issue in agricultural digitalisation, particularly around farmers' economic interests, privacy, confidentiality, access and data sharing.
AI is only as good as the information it receives. Poor or incorrect data can produce poor recommendations.
THE FUTURE FARM MAY BE MORE CONNECTED
The farm of the future could have sensors in the soil, weather stations in the field, drones above crops, satellites monitoring fields, machinery recording its own performance and livestock systems monitoring animal behaviour.
AI could bring much of this information together.
Instead of waiting until a problem becomes visible, farmers may increasingly use predictive systems to identify risks earlier — whether those risks involve drought, disease, pests, soil variability or changing weather conditions.
FAO is already working toward AI-enabled agricultural advisory systems and has highlighted the potential for AI to improve prediction of drought, disease outbreaks and weather-related shocks.
But there will also be challenges. Connectivity, cost, training, data ownership and the reliability of AI recommendations will determine how useful these systems become.
THE FARMER STILL MAKES THE DECISION
Artificial intelligence is another tool entering the farmer's toolbox.
Just as tractors did not eliminate agricultural knowledge, and GPS did not eliminate the need for a farmer who understands the land, AI should be viewed as a technology that can support better-informed decisions.
The farmer remains responsible for understanding the land, animals, markets and risks.
The real opportunity is to combine generations of practical farming knowledge with new generations of technology.
The history of AI began with the question of whether a machine could think. For agriculture, the more practical question now is different:
Can we use intelligent technology to help farmers produce more efficiently, manage resources better, reduce unnecessary costs and prepare for an increasingly uncertain future?
The technology is developing rapidly. The farmers who understand how to use it — while still trusting experience, good agricultural practice and sound judgement — will be part of an entirely new chapter in the history of farming.
