Could AI Make Farming Or Break It?

Previous agricultural revolutions have had unforeseen consequences, and the same could be true with AI


‘Man's survival, from the time of Adam and Eve until the invention of agriculture, must have been precarious because of his inability to ensure his food supply.’

Those are the words of Dr Norman Borlaug, a Nobel prize winning plant biologist who is lauded for successfully breeding high-yielding dwarf wheat varieties. These produced two to three times more grain than the traditional varieties of seed and became known as miracle seeds, leading to the so-called Green Revolution of the mid-20th century and supposedly saving over a billion people worldwide from starvation. Bruce Alberts, President of the National Academy of Sciences, USA, once said that, ‘Some credit him with saving more human lives than any other person in history.’

However, while many still praise Borlaug, there are also dissenting voices who doubt his contribution. Ecologist Vandana Shiva in India, for one, claims that Borlaug’s innovations led to reduced soil fertility, reduced genetic diversity, soil erosion and increased vulnerability to pests. Shiva wrote that, ‘Not only did Borlaug's 'high-yielding' seeds demand expensive fertilisers, they also needed more water. Both were in short supply, and the revolution in plant breeding was said to have led to rural impoverishment, increased debt, social inequality and the displacement of vast numbers of peasant farmers.’ John Perkins added in his book 'Geopolitics and the Green Revolution’, ‘If success means an increase in the aggregate physical supply of grain, the green revolution was a success. If success means an end to hunger, then the green revolution was a failure. People without access to adequate land or income, regardless of their country of residence, remain ill fed.’ The final word comes from Christopher Reed, Borlaug’s obituarist, who argued that ’Few people at the time considered the profound social and ecological changes that the revolution heralded among peasant farmers.’

History gives us, broadly, four agricultural revolutions. The first was when we moved from hunting and gathering to farming and herding, the second which went hand-in-hand with the arrival of mass consumption fuelled by the Industrial Revolution. The Green Revolution was the third. We are currently in the early stages of the fourth - one driven by AI, big data, and IoT.

The global AI market in agriculture industry is projected to grow at a CAGR of 22.68% during the period 2017-2021. Connected devices too will grow to an estimated 75 million in agriculture by 2020, while the average farm is expected to generate an average of 4.1 million data points every day in 2050, up from 190.000 in 2014. This has the potential to greatly increase the yield of farmland under tillage, with analysis of the data produced providing farmers with insights that enable them to operate far more efficiently, and machine learning algorithms used in drone technology planting and fertilizing seeds at a speed beyond human abilities. Prospera Chief Executive Daniel Koppel said in a statement, ‘While the agriculture industry has been somewhat slow to adopt information technologies, it is now closing the gap with state-of- the-art data processing tools, artificial intelligence, and machine learning.’

A number of organizations are already using AI for this purpose. One team of researchers at Penn State and the Swiss Federal Institute of Technology (EPFL), for instance, have fed a network of computers with over 53,000 photos of both healthy and unhealthy plants in an attempt to recognize specific plant diseases. Such technology will provide the basis for field-based crop-disease identification using smartphones. The system has been able to identify both crops and diseases – from photos – with an accuracy rate of up to 99.35%. Another has been developed by the Queensland University of Technology (QUT), who have found a solution to weeds in their AgBot II, a solar-powered machine with the potential to save millions of dollars and boost sustainability in the sector by beating one of the biggest enemies of farmers - weeds. AgBot II uses myriad sensors, a special software, and learning algorithms to move through the field, detect weeds, and destroy them. The destruction is achieved through the application of chemicals, where their amount is accurately measured to reduce any waste. According to QUT, the technology can reduce costs for weed destruction by 90%.

These are undoubtedly positive developments. However, as in the Green Revolution, we could be so focused on producing the food required to feed people that we are forgetting to consider the societal impact it could have. Nearly 99% of farms in the US and across the world are family-owned, and the vast majority of these are small farms. Small farmers are incredibly vulnerable, operating with tight margins and in a complex environment in which they are at the mercy of numerous factors outside of their control, such as the weather. Technology as powerful as AI could help them, but it first needs to reach farmers in places where starvation is actually an issue at a price they can afford, so that the tools do not end up concentrated in the hands of a few. The impact on jobs also needs to be considered. It is not enough to assume that jobs will be created from somewhere simply because they have been in this past. Economist David Autor points out that in 1900, more than 40% of the population worked in agriculture, but by 2000, that was down to 2%, thanks to the efficiencies introduced by farming machines. By the end of this century, it will likely be far below 1%. Demand for food is increasing rapidly as the population booms, while the supply of high-quality arable land is falling due to urbanisation, land deregulation, and climate change. Food and agribusiness is a $5 trillion market globally, and if this isn’t fairly distributed, AI will have failed.


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