You wake up just before sunrise, the birds and insects just beginning their paean to the earth’s damp fertility. After putting on comfortable clothes, you start up a pot of coffee. You’re ready to start farming.
So, you turn on your smartphone, and scroll through the analytics. You’re interested in finding a cost-effective soybean variety optimized for your farm’s unique conditions, so you share your analytics with a site specializing in such decisions. A machine-learning program suggests a good fit for your needs and puts you in touch with a genetics lab who can help develop this varietal.
You sip your coffee. An alarm goes off on your phone, notifying you that the root irrigation network you installed will be starting another watering cycle. This, too, is a process regulated by algorithms responding to real-time soil and weather data gathered from sensors placed strategically throughout your farm.
This regularly-scheduled watering reminds you to check on your harvest predictions. Sure enough, its yield and quality promise to exceed last year’s bumper crop, a reassuring thought to you as you place two slices of bread in the toaster.
An alert bell now sounds, drawing your attention from the toast that’s just sprung up. Sensors have detected the presence of weeds on one corner of your property. A fine-tuned AI program reads the data and prescribes a treatment regimen based on past outbreaks. You adopt this therapy, and then decide to walk the farm’s grounds.
Sound far-fetched? It isn’t. Far from science fiction, the scenario sketched above depends on technology already in development. If you thought AI and machine learning were going to change the future of our economy, wait till you see how it’s already changing how we farm.
While it’s impossible to follow every way AI is transforming agriculture, we’ve picked out four big ways it’s reshaping the field. Ready?
- Selective Breeding
Until recently, developing new crop strains was a tedious, time-consuming process. Teams of scientists in lab coats and latex gloves played games of trial and error with genetics until they got it right.
By substituting algorithms for hours of lab work, machine learning can cut that time to a sliver. A raw dataset, specified parameters, a set of outcomes, and an algorithm, and a program can spit out new recipes specially adapted for your farming niche. The science is already there; it’s just a matter of popularizing these applications.
- Environmental Management
Efficiency will be the key to succeeding in an agricultural industry trying to supply the demands of a growing population amid global competition and a rapidly-changing climate.
Here again, predictive algorithms that adjust for crop conditions using real-time soil and climate data can help monitor basic environmental factors like watering, fertilizer applications, and harvesting times.
3. Threat Detection
Pesticide costs — economic and environmental — are a drain. Conventional farmers either preempt weed growth and parasitic infestations by overapplying throughout the season or overreact to incipient outbreaks of problems.
AI can help inform a more efficient and environmentally-conscious approach. Drawing on raw data, it will project the impact of the outbreak and develop a treatment program that fits your needs and resources.
4. Yield and Quality Prediction
Farming has always involved educated guesswork. This is particularly so when it comes to forecasting what harvest results will look like.
As data collection on the farm and machine learning algorithms improve, hard evidence will start to ground expectations, allowing farmers to manage their crops better and plan more effectively for the future.
From seed to harvest, AI and machine learning are already at work changing how we farm. Ready or not, it’s here. Are you ready to reap the benefits?