How technology can prevent food shortage crises

agriculture plants

We’re being warned for years that the world is heading toward a food shortage crisis. The UN predicts that food production must double by 2050 to meet the needs of the planet’s steadily increasing population. Meanwhile, resources and land are becoming more scarce, and global warming is making things worse.

In fact, this is nothing new. Humanity has been dealing with food shortage since its dawn, and at every turn, it has found a solution to overcome the challenges. Agriculture was humankind’s first response to food shortage in an era where it relied on hunting for sustenance.

In the 20th century, advances in fertilizers, irrigation and mechanized farming helped feed a fast-growing population.

But at this stage, something else is needed to make sure the same amount of land, water and resources can feed the 9-10 billion people who will be living on Earth in the next 40 years.

Humanity’s way out from the next phase might be paved by fast growing technology trends such as Internet of Things, robotics and artificial intelligence, all of which are opening up new possibilities in numerous fields and industries.

Here’s some of the ways technology can help deal deal with the food shortage problem before it turns into a crisis.

Internet of Things

IoT, one of the fastest-growing sectors of the tech industry, has enabled us to expand the internet beyond our desktop, laptop and mobile devices to the physical world that surrounds us. By 2020, there will be more than 20 billion connected devices across the world.

Smart sensors will account for a large part of these devices, and they will gather data about the physical state and quality of things, including soil, plants, seeds, etc.

The gathered data can be used to glean insights and perform precision farming, such as applying water to areas where the moisture of soil has dropped instead of wasting water on huge patches of land that don’t need it. An IoT-managed watering system can considerably decrease consumption while at the same time increasing yields.

Added to that are the remote control capabilities of IoT and industrial IoT systems, which will enable minute changes and automated changes to be applied to agricultural machinery and equipment.

Drones and robotics

As humans become more urbanized, farmfield workers will become more scarce. However, demand for food will not lessen and will only increase. The void left from humans leaving rural areas for cities can be filled with drones and droids that are much more capable, flexible and affordable than heavy machinery—and much more hardworking than humans.

While being able to replace humans in fields, a combination of IoT and laser equipped drones can also perform tasks with higher precision, and apply energy and resources to the exact spots and locations that are needed.

For instance, using IoT sensors and weed detection software, farmer drones will be able to apply herbicide or laser exterminators to the exact location where it is required instead of spreading huge amounts of chemical substance on wide areas where it will go to waste or do more damage than good.

Machine learning and analytics

When combined with machine learning and analytics, data collected from IoT sensors can open up totally new possibilities. For instance, in the livestock business, by collecting the huge amounts of data collected by IoT sensors and feeding it to cloud-powered machine learning algorithms, livestock farmers will be able to glean actionable insights that will enable them to improve production.

By collecting sensor-generated data from cows and ingesting it, farmers can analyze and improve the quality, mixture and timing of the feed and increase milk yield without increasing the amount of feed.

Furthermore, machine learning and deep learning can be used to detect problems faster than humans. An image analysis algorithm fed with photos of diseased and healthy plant leaves, from which it learns to automatically scan image updates of fields and detect the health status of leaves and areas that are problematic and need attention.

Other uses of machine learning in agriculture include algorithms that consolidate weather forecasts and environmental data for different areas and help make predictions such as the emergence of pest or loss of nutrients, which can help farmers take action before damage is done. Predictive maintenance is one of the strongest uses of the convergence of IoT and machine learning.

What’s next?

This is just scratching the surface. The possibilities are a lot more, and I’m sure many of you out there have some great ideas to share. I’ll be writing about this again very soon, and I’m eager to hear what your innovations and ideas are in the field.

The truth is, we’re heading toward some very critical conditions regarding the availability of food for every human being on earth. But fortunately, we have a lot of tools and technologies that can help us overcome this challenge, just as we’ve done throughout the history of mankind. Now’s the time to act to make sure our children and children’s children will live in a world of abundance and comfort instead of one wrought with scarcity and strife.

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