4 Ways Artificial Intelligence will Drive Digital Transformation in Agriculture

The United Nations reports that about 1/3 of the food produced globally each year is lost or wasted, and I’d reckon that number is not too surprising. Those of us in the United States see evidence of waste each time we go out to eat or do a weekly purge of jam-packed refrigerators. Outside the waste, however, there’s a greater problem many of us don’t realize. Just as the amount of food wasted globally is skyrocketing, the global demand for food is, ironically, set to rise.

With exploding populations, global warming, and less land available for cultivation, we’re actually facing a global food shortage of epidemic proportions. How will we manage to feed and sustain 9 billion humans estimated to populate planet earth by 2050? And how will we support the 59-98 percent increase in food consumption that population is likely to need? Like many issues humans are facing in the world today, we are seeing digital transformation in agriculture, most specifically in the form of artificial intelligence (AI).

Sensors and Data

By far, the greatest development in agricultural technology (AgTech) comes in the form of connected sensors and the IoT. As you’d expect, successful agricultural production in digital transformation is becoming a numbers game. With the help of AgTech, connected farmers are beginning to share data, and make improvements in input, efficiencies, and operations processes, largely due to AI-driven sensors. These sensors can be ground, aerial, or machine-based, and all hold huge potential for agricultural production.

On the ground, for instance, sensors can monitor the quality of plants, soil, animal health, and weather. They can determine the best place to plant for the highest yield, and how much to plant to prevent waste. In the air, drones and satellites can monitor crop health and pest disease, preventing the surprise of a lost crop at harvest time. Farm equipment can also capture data on anticipated crop production. For instance, high-speed planting equipment can provide “as planted” estimates on crop yield and harvest output, allowing farmers to plan for sales forecasting, overflow and shortage. That’s not all. Robotic harvesting equipment can even use AI to pick ripe fruit and vegetables at just the right time, saving time, manpower, and waste. Talk about digital transformation in agriculture!

John Deere is just one company doing “precision ag” well today, developing technology to help connected farmers determine where best to plant and when to harvest. They can even help farmers manage equipment remotely from a centralized control center, allowing for even greater time efficiencies. (It goes without saying that when it comes to digital transformation in agriculture, the companies that thrive will be the ones that move from tractor provider to tech provider most seamlessly. Kudos to John Deere.)

Still, the benefits aren’t just to the farmers. Blue River Technologies, for instance, shows that they can reduce the use of herbicides by 90 percent by moving from broadcast spraying to targeted spraying using data pulled from AI sensors. Less herbicides are good for all of us—human and earth alike. Clearly digital transformation in agriculture isn’t just good for food production, it’s good for the health of the planet.

Research and Development

Just like AI is helping speed up pharmaceutical trials by decreasing the length of the trial and error phase of development, it’s doing the same thing for agriculture. The AI teams at Monsanto, for instance, found that algorithms could help them more quickly determine which hybrid plants would grow best in real-life planting conditions, saving massive amounts of product development time. For instance, in the past, Monsanto would evaluate corn hybrids for years in the field before bringing them to market—a process that could take eight years from discovery to commercialization. The breeding program would select about 500 breeds for trials—a process that was cost and time prohibitive. Using an algorithm that used the past 15 years of molecular marker and field trial information, they’ve shaved an entire year off the breeding process. That’s an incredible leap, especially considering the population surge we’re about to encounter in coming decades. What’s even better: connected farmers globally will theoretically be able to share this type of information, allowing for greater and faster product development not just on Monsanto farms, but around the world.

Image Recognition

Another exciting development: image recognition in AI. Google is working to train AI to recognize 5,000 species of plants and animals, which would improve drone ability to detect pest disease and crop damage. This advancement is huge, as it would allow farmers to monitor their acreage far more quickly and accurately than they ever have before, and to understand pest patterns over time.

Reaping the Harvest of Digital Transformation in Agriculture: More Numbers and Bodies are Needed

Despite the huge potential in AgTech right now, there are also a few concerns. For one, like any AI process, AgTech relies on data. But in a market where data is produced at annual intervals, data collection can be slow and difficult. The agricultural field has also found it difficult to compete with other tech-savvy industries in attracting young AI talent. I’m hoping a younger generation, wanting to find purpose in their work, will be drawn to this promising market. Because when it comes to digital transformation in agriculture, there is potential to impact not just farmer productivity, but billions of human lives.

The original version of this article was first published on Forbes.