Using AI in agriculture could boost global food security, but we need to anticipate the risks
Farmer's Weekly|April 22 & 29, 2022 - Double Issue
Asaf Tzachor, a research affiliate at the Centre for the Study of Existential Risks at the University of Cambridge in the UK, outlines the findings of a recent paper that looked at the risks involved in the roll-out of advanced and autonomous technologies in agriculture.
Asaf Tzachor
Using AI in agriculture could boost global food security, but we need to anticipate the risks

As the global population has expanded over time, agricultural modernisation has been humanity’s prevailing approach to staving off famine.

A variety of mechanical and chemical innovations delivered during the 1950s and 1960s represented the third agricultural revolution. The adoption of pesticides, fertilisers and high-yield crop breeds, among other measures, transformed agriculture and ensured a secure food supply for many millions of people over several decades.

Concurrently, modern agriculture has emerged as a culprit of global warming, responsible for one-third of greenhouse gas emissions, namely carbon dioxide and methane.

Meanwhile, inflation on the price of food is reaching an all-time high, while malnutrition is rising dramatically. Today, an estimated two billion people are afflicted by food insecurity (where having access to safe, sufficient and nutrient-rich food isn’t guaranteed). Some 690million people are undernourished.

The third agricultural revolution may have run its course, and as we search for innovation to usher in a fourth agricultural revolution with urgency, all eyes are on artificial intelligence (AI).

AI, which has advanced rapidly over the past two decades, encompasses a broad range of technologies capable of performing human-like cognitive processes, such as reasoning. It’s trained to make these decisions based on information from vast amounts of data. In assisting humans in fields and factories, AI may process, synthesise and analyse large amounts of data steadily and ceaselessly. It can outperform humans in detecting and diagnosing anomalies, such as plant diseases, and making predictions, including those about yield and weather.

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