Before the tractor was invented, farmers worked their land tirelessly alongside beasts of burden reminiscent of horses and mules that required six acres of land for feed, per animal, every year.
Fearing change, job substitute, or cost, farm owners didn’t exactly rejoice and were generally skeptical of the tractor. Still, its use eventually became standard by the early twentieth 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 enhance their business operations, it also helped complement 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 plenty of are counting on AI to have the identical effect as food insecurity climbs.
But why should the agriculture industry embrace AI, and can it provide enough assistance fast enough to stop food insecurity?
Why agriculture needs AI’s efficiency now
Wheat farmers in Egypt struggle to provide their crops with enough water, and vegetable growers in California are experiencing unexpected extreme weather conditions. But global agriculture is battling greater than just the environmental impact of climate change. The industry faces an extended, diverse list of problems and disruptions that can further inflate food insecurity figures if not quickly corrected.
Climate change threats are existential, nevertheless, labor issues impact every aspect of agriculture. Much of the Western world relies heavily on experienced seasonal migrant labor to assist work long, strenuous days within the fields. Still, disruptions brought on by the COVID-19 pandemic and other destabilizing aspects left many farms short-handed. As well as, 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 expert employees isn’t so simple as plucking a random person off the road. 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 instance, the continuing Russian war with Ukraine—a region generally known as “Europe’s breadbasket”—has severely plugged food supply flows, especially to parts of the world already affected by food insecurity like Africa.
Moreover, rising input costs, shrinking production values, and shifting markets are declining productivity in lots of 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 show to AI to complement shrinking profits and meet global demand.
But step one is for tech providers to construct trust with growers, which may occur by highlighting where AI is already making a large 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 anticipated global population of 9.1 billion people in 2050. That’s a tall order for any grower to satisfy while also considering climate impacts without proper technology.
In lots of industries, AI applications are more theoretical and wish time to undergo testing and quality assurance. Healthcare, a main example, begs for AI help, but its current use is restricted because of concerns surrounding data privacy and malpractice.
But in agriculture, we’re seeing farms and farmers empowered by latest AI applications—including smaller, local growers who don’t have the resources to soak up the impact of pandemics, wars, or climate change.
Because the second largest exporter of agricultural products globally and one in every of the more densely populated countries, the Netherlands has at all times needed revolutionary 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 watch plant health, and make data-driven decisions in farms and greenhouses.
Last yr, the Netherlands Organisation for Scientific Research (NWO) established a latest institution integrating plant biology, computational modeling, and AI to develop crop varieties that could be more resilient to climate change and fewer depending on chemical crop protection.
The U.S. is blessed with an abundance of possibly the most effective farmland. Nonetheless, the common American farmer is almost 60 years old, with nearly 40 percent being over the age of 65. To assist aging and short-staffed growers, AI and robotics have gotten 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 may boost crop health and improve yields.
With higher-quality data and refinements in ML, computer vision, deep learning, and revolutionary robotics, AI is actively helping growers make agriculture a more viable business endeavor, more sustainable, and more efficient overall.
For instance, data sharing and collaborations between growers and tech providers can assist spread invaluable information that enhances productivity and crop knowledge, enabling AI systems to enhance while allowing growers to achieve invaluable insights. AI and data sharing may even help alert farming communities of latest crop threats spreading in a particular region. Whether using AI tools or the information it generates for crop monitoring or predictive analytics, it’s relevant ammunition within the battle to spice up food security.
Rising food insecurity will ultimately result in economic hardships, conflicts, and widespread destabilization affecting all elements of humanity. Avoiding these catastrophic scenarios demands expanding and spreading AI’s positive impact on agriculture.
