Before the tractor was invented, farmers worked their land tirelessly alongside beasts of burden such as horses and mules that required six acres of land for feed, per animal, each year.
Fearing change, job replacement, or cost, farm owners didn’t exactly rejoice and were generally skeptical of the tractor. Still, its use eventually became standard by the early 20th century, enabling farms of all sizes and crop types to plow and cultivate the land more efficiently. The tractor didn’t just offer farmers a tool to improve their business operations, it also helped supplement food supplies.
As AI disrupts nearly every industry, the agriculture sector, which faces significant obstacles on multiple fronts, is cautiously embracing machine learning, computer vision, and other data-driven processes. The tractor led to the embrace of other inventions that triggered the Green Revolution, and many are counting on AI to have the same effect as food insecurity climbs.
But why should the agriculture industry embrace AI, and will it provide enough assistance fast enough to stop food insecurity?
Why agriculture needs AI’s efficiency now
Wheat farmers in Egypt struggle to supply their crops with enough water, and vegetable growers in California are experiencing unforeseen extreme weather conditions. But global agriculture is battling more than just the environmental impact of climate change. The industry faces a long, diverse list of problems and disruptions that will further inflate food insecurity figures if not quickly corrected.
Climate change threats are existential, however, labor issues impact every aspect of agriculture. Much of the Western world relies heavily on experienced seasonal migrant labor to help work long, strenuous days in the fields. Still, disruptions caused by the COVID-19 pandemic and other destabilizing factors left many farms short-handed. In addition, changing social pressures and lifestyle considerations are leading many youth from farming backgrounds to shun the fields and orchards for jobs in hi-tech or other more attractive professions.
A labor shortage is one thing, but replacing skilled workers isn’t as simple as plucking a random person off the street. Critical roles like scouting, harvesting, and managing irrigation systems require expert knowledge and training to be performed acceptably.
Wars and labor disruptions further exacerbate food insecurity by disrupting supply chains. For example, the ongoing Russian war with Ukraine—a region known as “Europe’s breadbasket”—has severely plugged food supply flows, especially to parts of the world already suffering from food insecurity like Africa.
Additionally, rising input costs, shrinking production values, and shifting markets are declining productivity in many farms and tanking growers’ profit margins. If this doesn’t make farming hard enough, climate change compounds all this, encouraging traditionally tech-resistant growers to turn to AI to supplement shrinking profits and meet global demand.
But the first step is for tech providers to build trust with growers, which can happen by highlighting where AI is already making a massive difference.
Where AI helps growers keep the food flowing
The UN’s Food and Agriculture Organization estimates that farmers must produce 70 percent more food to feed the predicted global population of 9.1 billion people in 2050. That’s a tall order for any grower to meet while also considering climate impacts without proper technology.
In many industries, AI applications are more theoretical and need time to undergo testing and quality assurance. Healthcare, a prime example, begs for AI help, but its current use is limited due to concerns surrounding data privacy and malpractice.
But in agriculture, we are seeing farms and farmers empowered by new AI applications—including smaller, local growers who don’t have the resources to absorb the impact of pandemics, wars, or climate change.
As the second largest exporter of agricultural products globally and one of the more densely populated countries, the Netherlands has always needed innovative approaches to overcoming its geographic limitations and preserving its land. With the historical memory of the 1944-45 famine, the Dutch have broadly embraced AI in agriculture to implement precision farming practices to optimize crop production, leverage computer vision to monitor plant health, and make data-driven decisions in farms and greenhouses.
Last year, the Netherlands Organisation for Scientific Research (NWO) established a new institution integrating plant biology, computational modeling, and AI to develop crop varieties that can be more resilient to climate change and less dependent on chemical crop protection.
The U.S. is blessed with an abundance of possibly the best farmland. However, the average American farmer is almost 60 years old, with nearly 40 percent being over the age of 65. To help aging and short-staffed growers, AI and robotics are becoming ever more common across U.S. farms, boosting the productivity of labor-intensive tasks like picking and plowing while providing data-driven insights to make informed decisions that can boost crop health and improve yields.
With higher-quality data and refinements in ML, computer vision, deep learning, and innovative robotics, AI is actively helping growers make agriculture a more viable business endeavor, more sustainable, and more efficient overall.
For example, data sharing and collaborations between growers and tech providers can help spread valuable information that boosts productivity and crop knowledge, enabling AI systems to improve while allowing growers to gain valuable insights. AI and data sharing can even help alert farming communities of new crop threats spreading in a specific region. Whether using AI tools or the data it generates for crop monitoring or predictive analytics, it’s relevant ammunition in the battle to boost food security.
Rising food insecurity will ultimately lead to economic hardships, conflicts, and widespread destabilization affecting all aspects of humanity. Avoiding these catastrophic scenarios demands expanding and spreading AI’s positive impact on agriculture.
