Agriculture embraces artificial intelligence
Graybeards may remember the thrill they felt when pencil-laden math calculations moved warp speed ahead into the calculator age.
These days, artificial intelligence (AI) promises to bring the same heat to agriculture that it did to math classes decades ago. Artificial intelligence is a technology that includes several subsets such as machine learning, says Rania Khalaf, Inari chief information and data officer.
“Machine learning enables computers to mathematically predict outcomes or make classifications by finding patterns in large amounts of data,” she says. “It then learns to update these patterns or classifications over time as it sees new data.”
“The biggest advantage of artificial intelligence is the ability to make complex calculations at a high speed that previously required a human to perform,” adds Kent Klemme, general manager of See & Spray for Blue River Technology. “The recent improvements in GPUs [graphics processing units] have provided the computing power to make this possible. It takes a lot of data to target specific problems.”
See & Spray Ultimate technology — powered by machine learning — enables sprayers to target just weeds while spraying among crops. “We’ve taken thousands and thousands of images of different weeds in different crops under different situations such as clear skies, cloudy skies, dark skies, different soils, and varying levels of residue,” says Klemme.
Blue River and John Deere data scientists then train the See & Spray Ultimate system to recognize plants under myriad conditions. These images are sorted out through algorithms, which involve repetition of one or more mathematical operation. Algorithms are often implemented and solved on computers.
Patriot 50 series sprayers from Case IH use a form of machine learning called vision guidance.
“It’s a row guidance solution that makes a steering command based on plant location,” says Chris Dempsey, global precision technology director for Case IH.
Vision guidance uses an on-board camera that relays corn plant location to the sprayer so it stays on the row rather than run over crop plants, he adds.
Artificial intelligence is a broad area that includes many subsets, such as machine learning. Basically, though, it uses reams of data to drive efficiencies, says Dempsey.
“The biggest obstacle that the whole agricultural industry faces in digital farming is taking large and complex data sets and turning them into meaningful insights,” says Ashwin Madgavkar, founder of Ceres Imaging. “AI can really help bridge that gap by synthesizing all this data into actions that a grower can take.”
Its use is increasing in crop breeding.
“We’re looking into all kinds of different new technologies, whether it’s machine learning or advanced analytical models to predict hybrid performance,” says Mike Popelka, AgReliant Genetics hybrid product breeder manager. “The [crop breeding] industry is going more toward these models.”
AI use is also commonly being used across many machinery lines. Case IH uses machine learning through 16 sensors that adjust its AFS Harvest Command system.
“Increasing throughput while decreasing [grain] losses is all now done automatically,” says Dempsey. “Historically, combine operators would have to make sieve adjustments if they had too much cob or foreign material in a grain sample.”
Machine learning now does this automatically.
“Sensors tell the combine it needs to shut down a lower sieve or increase fan or rotor speed,” he says. “Those adjustments are based on knowing what foreign material or bad grain quality looks like in a known database of a given crop type. It’s basically a database of images showing good [quality grain] from bad.
