Launched in 2019, the program pairs Deere with hand-selected startups for year-long, project-based collaborations designed to test how emerging technologies perform in agricultural and construction use cases. It is not primarily an acquisition or investment vehicle.

“We’ve intentionally designed it that way,” said Colton Salyards, who manages the program within Deere’s corporate development and strategy group.

“The program was never designed to be an investment or an acquisition vehicle.”

WHY IT MATTERS: Emerging sensing and AI technologies could eventually improve soil analysis, equipment uptime and precision decision-making on farms.

Instead, Deere and each startup define a joint project, outlining objectives on both sides and evaluating how a given technology might perform in agricultural or construction use cases.

With the addition of this year’s five companies, Deere will have worked with 42 startups through the program.

Each year, Salyards said, public announcements of the cohort generate significant inbound interest from startups hoping to participate. The response can be “overwhelming,” but the companies selected stand out.

“There’s a key reason why we’ve selected them,” he said.

“There are use cases across agriculture that we believe could be of tremendous customer value.”

Sensor sensibility

Among the 2026 cohort is Australian firm resonAg, which is adapting miniaturized MRI-based sensing technology — technology originally developed for medical imaging, and later adapted for industries such as mining and oil and gas — for use in advanced soil sensing.

Deere is exploring how that sensing capability could support precision agriculture applications.

“This is of huge importance for precision agriculture,” he said.

“Imagine a planting system that can sense and act in real time to conditions across the field.”

Another company, AIRS ML, is developing edge-AI systems that combine machine sensor data with on-device machine learning to predict equipment failures in real time. The goal is to improve uptime by identifying potential maintenance issues before they lead to breakdowns.

The remaining companies in the cohort include:

• IoTag, which focuses on telematics and mixed-fleet performance insights.
• TorqueAGI, which is developing AI foundation models for robotics.
• Aerobotics, which applies drone imagery and computer vision to specialty crop production.

While not designed as an acquisition vehicle, the program has, in two instances, led to investment or acquisition when the strategic fit aligned. Salyards emphasized that integration into Deere equipment is not the default outcome.

“This is one vehicle among many that we use to understand what innovative companies are out there,” he said.

“Ultimately, it helps us determine how well those technologies could fit for our ag and construction customers.”

The post Deere taps startups to explore sensing, AI tech for farm uses appeared first on Grainews.

Artificial intelligence, automation and data systems dominated nearly every session, from crop protection to robotics to biotech discovery.

However, beneath all that, one quieter theme kept surfacing.

A lot of the early, practical value of these systems is not in running machines. It is in interpreting data and turning it into recommendations.

WHY IT MATTERS: As AI tools take on more of the data work, farmers will still need trusted advice to turn those recommendations into decisions that work in their fields.

In fact, based on the discussions at the summit, that part of the conversation was in the rear view mirror. Much of the focus now is on what comes next — building systems that can act on those recommendations.

Soil tests, weather stations, satellite imagery, equipment data is familiar ground for agronomy. What is changing is how quickly and how consistently that information can be processed.

In one session about biotech discovery, speakers described AI systems that can design and refine experiments with minimal human input.

It is a long way from a Prairie field, but it is easy to imagine that same approach being used to improve plot trials or even guide on-farm decisions aimed at maximizing yield.

And it is already happening.

On the farm, that same capability is showing up in decision support — not perfect, not complete, but improving. These tools are getting better at taking large volumes of information and turning it into clear, actionable decisions.

From interpretation to action

And that raises a fair question. If more of that interpretation work can be done by a system, where does that leave farm agronomists?

The answer is not that they disappear. It is that the job shifts.

Research agronomists are not really in the crosshairs here. They are still building the knowledge base. The question is what happens to the people turning that knowledge into decisions on the farm.

That kind of agronomy has never just been about reading numbers off a report. It is about context: knowing the field, the farmer, the equipment and the risks they are willing to take.

A recommendation generated from data still has to be weighed against reality. Is the field fit? Does the timing work? What happens if the weather turns? Does it fit the rest of the rotation?

Those are not problems that go away with better models. In some ways, they become more important because more recommendations are coming, faster and with more confidence behind them.

What these systems may change is how agronomists spend their time. Less time pulling data together. Less time building base recommendations from scratch. More time stress-testing those recommendations, adapting them to local conditions and helping farmers decide what to act on and what to ignore.

There is also a practical layer to this that did not get as much attention on stage. These tools do not just appear on farms fully formed. They need to be set up, calibrated and understood. Someone has to translate them from a product into something that actually works in a field.

One discussion on soil health touched on a more basic issue: even something as fundamental as soil testing is not fully standardized. Results can vary depending on how samples are taken, handled and processed.

That is an opportunity.

It suggests there is still a role for the local private agronomist — someone who knows the region and their customers, understands local soil conditions, along with insect and disease pressure, and someone who farmers know personally and trust.

The role doesn’t disappear, it changes

It is easy to frame new technology as a threat to existing roles, but agriculture has22s a way of absorbing new tools and reshaping the jobs around them.

GPS did not eliminate the nesed for farm agronomists. Variable rate did not either. They changed the conversation.

This one feels different. These systems are starting to take on the interpretation work that has traditionally defined farm agronomy. However, the pattern is familiar.

The technology is moving quickly, that much is clear. However, it is still being tested against the same reality. Fields, weather and economics have a way of exposing weak ideas.

On-farm agronomy does not sit outside that process. It is part of it.

If anything, the need for people who can bridge the gap between what a system suggests and what actually works on the ground will only grow.

The post How AI is changing on-farm agronomy and decision-making appeared first on Grainews.

Called the Cubix, it’s the latest in large square balers from the German farm equipment manufacturer, winning a gold Innovation Award at Agritechnica last November.

So what makes it special?

The driveline on the Cubix is integrated into the frame to deliver direct power flow to the baler. Along with a pair of 202 kilogram flywheels, this helps put the Cubix’s output as high as 70 tonnes per hour in the field.

“For the starting process, you do not need a big tractor,” Daniel Moersch, Claas’s product manager for square balers, said at Agritechnica 2025 in Hanover, Germany.

By engaging only one flywheel at a time, the Cubix is also easier and more efficient to start.

Once the first flywheel reaches 1,650 r.p.m., the second flywheel engages. Once the speed of both flywheels match, a pair of clutches on each side of the baler kick in to get the rotor operational.

While a 250-horsepower tractor gets the Cubix running and baling hay in the field, Moersch said an operator won’t reach the high throughput the Cubix is capable of with that amount of horsepower. For that, he recommended tractors have 400 h.p. or higher.

The Cubix also uses a double loop knot design to secure bales. This helps eliminate twine waste and increases tensile strength compared with other bale knot designs.

Its six-knotter system also reduces the amount of twine needed and the time it takes to change twine.

Artificial intelligence has been incorporated into the Cubix’s design when it comes to controlling bale density.

AI-supported software also assists in maintaining target ranges of density, throughput and bale length.

If an overload is detected during operation, the Cubix automatically decouples the rotor and pick-up before a blockage can occur.

At this stage in its design, the Cubix requires a tractor to operate in the field, but future designs may not require this.

“We are looking at an all-automatic driving baler,” said Moersch.

“For the moment, we are within the tractor implement system.”

Moersch said Claas plans to have a pre-series launch of the Cubix baler in 2027 with a complete launch sometime in 2028.

The post New Cubix baler from Claas aims for high output appeared first on Grainews.

The importance of using critical thinking skills and not taking anonymous online posts at face value have never been more important, no matter what the topic.

Unfriendly governments are putting a lot of effort into attempting to mislead and misinform populations in western nations.

In 2024, security services in Canada, the Netherlands and the United States shut down almost 1,000 “bot” farms related to the Russian government. They were spreading disinformation on X (formerly Twitter) on a variety of topics, including things like vaccine conspiracy theories.

And there are others who want to generate click bait for their own gain.

Some of those fake posts have recently begun targeting the machinery brands, claiming announcements relating to introductions of vehicles and equipment that simply don’t exist.

There are several posts circulating now with impressive images claiming Caterpillar is about to launch a new pickup truck, which it isn’t. However, the AI-generated pictures of it are pretty convincing.

Then there’s the claim General Mtors has developed a Silverado look-like Class 8 highway tractor. It hasn’t. The pictures of it, too, look very real.

For its part, John Deere has had enough of the fraudulent posts that it’s about to launch a pickup truck. The green brand recently made it clear that is a hoax.

A post on the Deere website reads, “You may have seen it: glossy green paint, a leaping deer logo, and a John Deere pickup that looks like it drove straight out of a farmer’s dream. There’s just one catch — it isn’t real. To be clear, we’re not assembling a John Deere truck.”

Although, it would be cool, wouldn’t it?

However, if you want to add a little bling to your real Ford, Chevy or Ram pickup (or whatever brand you have), well, John Deere’s online store or local dealership actually can help you with that.

The post Be wary of AI fakes on social media appeared first on Grainews.

A new mobile app in development at the University of Saskatchewan promises to identify field insects instantly, show local populations on a live map, and deliver management advice based on crop, region and weather.

Why it matters: Tracking pests and beneficial insects in real time could help farmers make quicker, better-informed pest management decisions and cut unnecessary pesticide use.

The app, called IPPM Now, is expected to combine artificial intelligence (AI), geospatial data and entomology expertise to turn a smartphone photo into real-time agronomic insight. Its developers say it recognizes both harmful and beneficial insects, from flea beetles and grasshoppers to pollinators and lady beetles, with more than 90 per cent accuracy.

Farmers could therefore learn not only what insect they’re dealing with, but whether pressure has reached economic thresholds and what conservation steps might protect beneficial species.

For developers, the goal is to pull together information that has long been scattered across research programs, scouting reports and grower experience.

“It will be super useful for farmers, agronomists and scientists scouting insect pests,” says project lead Teresa Aguiar, a PhD student at the University of Saskatchewan. “Scouting takes a lot of time, and the information from researchers, agronomists and farmers is often disconnected.”

From photo to field map

Each image submitted through the app is tagged to a rural municipality, not to an exact GPS point, to protect user privacy. Those records build a colour-coded map that shows where pests, pollinators and biocontrol insects are active. The development team plans to integrate local weather data so future versions can forecast outbreaks and pollinator activity.

“We want a practical tool that integrates insect identification, spatial reference, data collection and management recommendations in one platform to make decisions with all the variables involved in pest management,” Aguiar says.

Smart traps and sweep-net scouting

Alongside the app, Insect Track Solutions, the Saskatoon-based startup commercializing the project, is testing a smart trap that marries a sticky card with a small camera. Set in a field, the trap automatically photographs insects and uploads images to the same AI model used by the app, identifying and counting adult insects without anyone having to check the card manually.

Because sticky cards only capture flying adults, Aguiar’s team also designed a simple, low-tech workaround for ground or juvenile stages.

“To solve this problem, make a sweep and then put that sweep in a ziplock bag with a white background and take a picture of that ziplock bag using our mobile app,” she says. The model can then identify and count nymphs and instars in the sample, giving a fuller picture of population levels.

What it can do today

The prototype version already supports canola and wheat and recognizes 10 key insect groups common to the Prairies. Pests include lygus, flea beetles, grasshoppers and weevils; beneficials include lacewings, lady beetles, hoverflies, bumblebees and honeybees. The app draws on field data and photo libraries, including data and images supplied by Manitoba entomologist John Gavloski, to keep improving its accuracy toward species-level ID.

Future updates will broaden crop coverage and add weather and growth-stage links to help predict pest risk or pollinator timing.

Beta testers wanted

Before IPPM Now officially launches next spring, the developers are inviting farmers, agronomists and crop scouts to test the app this winter. Beta users will get early access, provide feedback on design and function, and can volunteer to host free smart-trap trials in 2025.

Aguiar says user input will guide the final version.

“We are sending beta testing invitations. If you’re interested, we can put the app in your phone, early access to give us feedback and help us to shape the app and get it ready for next season.”

Farmers and agronomists interested in early testing or hosting field validation sites can contact Insect Track Solutions Inc. via email.

The post USask-led startup testing AI insect management app for Prairie growers appeared first on Grainews.

Access to reliable, timely weather forecasts can help farmers prepare for the weeks ahead, find the best time to plant or determine how much fertilizer will be needed, resulting in better crop yields and lower costs.

Yet, in many low- and middle-income countries, accurate weather forecasts remain out of reach, limited by the high technology costs and infrastructure demands of traditional forecasting models.

A new wave of AI-powered weather forecasting models has the potential to change that.

By using artificial intelligence, these models can deliver accurate, localized predictions at a fraction of the computational cost of conventional physics-based models. This makes it possible for national meteorological agencies in developing countries to provide farmers with the timely, localized information about changing rainfall patterns that the farmers need.

The challenge is getting this technology where it’s needed.

Why AI forecasting matters now

The physics-based weather prediction models used by major meteorological centers around the world are powerful but costly. They simulate atmospheric physics to forecast weather conditions ahead, but they require expensive computing infrastructure. The cost puts them out of reach for most developing countries.

Moreover, these models have mainly been developed by and optimized for northern countries. They tend to focus on temperate, high-income regions and pay less attention to the tropics, where many low- and middle-income countries are located.

A major shift in weather models began in 2022 as industry and university researchers developed deep learning models that could generate accurate short- and medium-range forecasts for locations around the globe up to two weeks ahead.

These models worked at speeds several orders of magnitude faster than physics-based models, and they could run on laptops instead of supercomputers. Newer models, such as Pangu-Weather and GraphCast, have matched or even outperformed leading physics-based systems for some predictions, such as temperature.

AI-driven models require dramatically less computing power than the traditional systems.

While physics-based systems may need thousands of CPU hours to run a single forecast cycle, modern AI models can do so using a single GPU in minutes once the model has been trained. This is because the intensive part of the AI model training, which learns relationships in the climate from data, can use those learned relationships to produce a forecast without further extensive computation – that’s a major shortcut. In contrast, the physics-based models need to calculate the physics for each variable in each place and time for every forecast produced.

While training these models from physics-based model data does require significant upfront investment, once the AI is trained, the model can generate large ensemble forecasts — sets of multiple forecast runs — at a fraction of the computational cost of physics-based models.

Even the expensive step of training an AI weather model shows considerable computational savings. One study found the early model FourCastNet could be trained in about an hour on a supercomputer. That made its time to presenting a forecast thousands of times faster than state-of-the-art, physics-based models.

The result of all these advances: high-resolution forecasts globally within seconds on a single laptop or desktop computer.

Research is also rapidly advancing to expand the use of AI for forecasts weeks to months ahead, which helps farmers in making planting choices. AI models are already being tested for improving extreme weather prediction, such as for extratropical cyclones and abnormal rainfall.

Tailoring forecasts for real-world decisions

While AI weather models offer impressive technical capabilities, they are not plug-and-play solutions. Their impact depends on how well they are calibrated to local weather, benchmarked against real-world agricultural conditions, and aligned with the actual decisions farmers need to make, such as what and when to plant, or when drought is likely.

To unlock its full potential, AI forecasting must be connected to the people whose decisions it’s meant to guide.

That’s why groups such as AIM for Scale, a collaboration we work with as researchers in public policy and sustainability, are helping governments to develop AI tools that meet real-world needs, including training users and tailoring forecasts to farmers’ needs. International development institutions and the World Meteorological Organization are also working to expand access to AI forecasting models in low- and middle-income countries.

AI forecasts can be tailored to context-specific agricultural needs, such as identifying optimal planting windows, predicting dry spells or planning pest management. Disseminating those forecasts through text messages, radio, extension agents or mobile apps can then help reach farmers who can benefit. This is especially true when the messages themselves are constantly tested and improved to ensure they meet the farmers’ needs.

A recent study in India found that when farmers there received more accurate monsoon forecasts, they made more informed decisions about what and how much to plant – or whether to plant at all – resulting in better investment outcomes and reduced risk.

A new era in climate adaptation

AI weather forecasting has reached a pivotal moment. Tools that were experimental just five years ago are now being integrated into government weather forecasting systems. But technology alone won’t change lives.

With support, low- and middle-income countries can build the capacity to generate, evaluate and act on their own forecasts, providing valuable information to farmers that has long been missing in weather services.

—Paul Winters is professor of sustainable development at the University of Notre Dame. Amir Jina is assistant professor of public policy at the University of Chicago.

The post AI is transforming weather forecasting − and that could be a game changer for farmers around the world appeared first on Grainews.

The company behind the CaseIH and New Holland brands has added a new AI-based feature to its MyCNHstore.com website.

Owners of CNH tractors looking for a replacement part can take a photograph of their worn out one and upload that photo to the website, which will then identify it and find the necessary replacement.

“It’s really quite simple to use,” says Paul Zirk, e-commerce marketing head at CNH.

“We have a little camera icon on the top search bar of the page. You can use it on a mobile or desktop device. Click that icon and it opens up a window. If you’re on your phone, you can take a picture of the part. If you’re on a desktop, you can upload an image, filter by model and then search.

“You’ll get back a list of parts that most closely match the one you searched for.”

The website can then be used to order the part through the dealership with which the producer prefers to do business.

The website will search that dealer’s inventory and let the producer know if the part is in stock there or if it has to be ordered from a CNH warehouse.

“When you go onto the website and search, you’ll see stock at your dealer and CNH stock,” says Zirk.

“For instance, if the dealer doesn’t have it on hand, you can tell how the stock is positioned based on your shipping options.”

As the photo identification system expands, it will eventually include the full current year lineup of equipment, but for the time being it can only be used on current model year tractors.

“For the visual search, right now we’re launching with current production,” he says.

“Affter we feel everything is absolutely sound, we’ll start rolling out other production lines until we get everything in current production. From there, we’ll take a look back and see how far back we can go.”

However, parts for a wide range of CNH machines can be located and ordered via the website using other manual search options other than the photo identification feature.

“The website mycnhstore.com really supports customers in general for older equipment as well,” says Zirk.

“We have catalogues for tractors going back to the ‘50s, if you want to look at assembly diagrams.”

The number of available parts and diagrams will vary for the older equipment.

Owners can also create a customer account that lists all their CNH equipment, which can speed up searching for replacement parts.

When accessing the website, producers should be sure to check that they’re on the Canadian site and not the U.S. page.

The post CNH launches photo feature to identify parts appeared first on Grainews.

A University of Manitoba soil scientist is using AI and satellite data to explore how Prairie landscapes respond to long-term stress — and how they bounce back.

At a recent presentation, Nasem Badreldin, who teaches digital agronomy at the U of M, explained how his team is using simulations to study landscape resilience. The model they built uses 25 years of daily satellite data to show how vegetation and soil systems shift under pressures such as drought, erosion or the loss of organic matter.

Landscapes move through zones of stability, recovery, degradation and vulnerability. Over time, repeated stress causes ecosystems to become less predictable and more fragile.

The key, Badreldin says, isn’t just understanding how ecosystems degrade. It’s also about identifying early signs of transition, when a system begins shifting from one state to another. For example, a system might shift from grassland to shrubland or from productive cropland into a degraded state where native vegetation or invasive species take over.

In some cases, there isn’t much of a transition — the changes can be immediate and dramatic, like a landslide or flood wiping out vegetation. These “sudden death” events, as Badreldin calls them, also provide useful information.

The challenge is understanding what’s driving the change. What seems like a drought effect might actually be due to declining soil carbon, resulting in reduced water-holding capacity — less obvious, but potentially more impactful. This gap in clarity is one of the hurdles keeping the research from practical application.

One early tool emerging from Badreldin’s Digital AgroEcosystems Lab is a web app his team informally calls TerraQuest.

Still in early development (version 0.2), the tool was built using Google Earth Engine. It lets users draw a custom area anywhere in North America to access satellite data, including NDVI, leaf area index, precipitation and solar radiation. While it’s currently designed for researchers, a future version could plausibly support on-farm monitoring of changing field conditions.

But to be useful on the ground, models must link cause to effect — and that remains elusive.

“The models up to now can’t catch those two,” Badreldin says. “We are trying to find a way to do that. We don’t yet know how — but we will.” — D.N.

The post Catching a landscape before it crashes appeared first on Grainews.

AgriEnhance

Agi3 Risk Services rolled out its AgriEnhance program in 2023.

The program uses data like legal land descriptions, normalized difference vegetation index (NDVI), climatic volatility, soil type and soil moisture, and combines it with farms’ crop plans, claims and production history to generate an individualized premium for the farm based on its specific risk profile. The process is powered by artificial intelligence.

Producers frequently use the program to complement AgriInsurance crop insurance, though some farms use it as standalone insurance alongside AgriStability said Kyle Gibson, Agi3’s managing director of operations. It can be used on an individual crop or whole-farm basis.

Premiums may vary based on if the canola is planted in the north end or the south end of the farm that year, Gibson said.

“We’re pricing your risk. We’re not pricing your neighbor’s risk into your premiums,” Gibson said.

Agi3 is looking to incorporate behavioral data into its insurance as well.

“We can link in and we can see your combine, and what it’s doing, how it’s being used. You can let us know some of your best management practices are,” Gibson said.

Based on this data, the farm’s score could go up and that will correlate to a discount.

“It’s monetary compensation by discounting.”

Agi3 is currently working with a select farms on this type of insurance.

ForwardProtect

Agi3 launched ForwardProtect this year. The program protects farms from penalties if they can’t fulfill forward contracts due to issues like yield shortfalls.

Agi3 founders Ray Bouchard and Lysa Porth saw the need for the program after many producers weren’t able to fulfill contracts after 2021’s severe drought, Gibson said.

“Since then, forward contracting has decreased significantly,” he said.

“Now we can come in there and give them the confidence to go after that price increase,” because the farmer knows they’ll be backed up should things go wrong, Gibson said.

ForwardProtect is also individualized to the farm.

FarmElevate

FarmElevate is a property and casualty insurance product designed to give strong coverage to “best in class” farms, said Gibson.

The program targets farms that are following best risk management practices, are more progressive with data sharing, have strong data integrity and accuracy, and may be growing or may have a lot of equipment that may not be fully covered by conventional insurance. For example, the farm may need higher loss-of-use coverage than offered by their current insurance policy.

The post Agi3’s AI-powered individualized farm insurance products win innovation prize appeared first on Grainews.

Farm Credit Canada and Results Driven Agriculture Research (RDAR) have unveiled a generative artificial intelligence tool that will deliver “timely advice (that) producers can use immediately.”

The tool is called Root.

FCC says it will help farmers adopt best practices, right from their phones.

“Root is more than a technology solution, it’s part of a broader effort to bring back something Canadian agriculture has lost: accessible, trusted and timely insight,” Justine Hendricks, FCC president and chief executive, said in a release.

“With the decline of local advisory networks (extension services), too many farmers and ranchers have had to rely on fragmented information or go at it alone. By partnering with RDAR, we’re helping producers access the kind of expertise that once came from decades of community-based knowledge sharing.”

Many agronomists, livestock specialists and extension experts would take issue with the idea that farmers no longer have trusted and timely advice.

Nonetheless, it is correct to say that government cutbacks have reduced extension services. There are fewer people on the Prairies that provide unbiased and relevant information to producers.

There was a time, maybe 30 to 40 years ago, when provincial government reps were the clear-cut leaders of ag extension across Canada.

Provincial agriculture departments still employ specialists in regional offices, who are responsible for delivering the latest research and best information to livestock and crop producers.

Shrinking provincial extension services

But the number of provincial extension specialists has shrunk.

In some provinces, they have almost disappeared.

In October 2020, the Western Producer reported that the Alberta government had laid off about 135 Alberta Agriculture employees who worked in primary agriculture. That included research and extension staff.

“People always forget that Alberta Agriculture had offices across the province and there was a lot of co-operative work that was done,” said Ross McKenzie, a retired department employee.

“That capacity will be lost. You’ll see (applied research) groups … kind of pick up and carry on, but you won’t have that co-ordinated effort across the province that we had.”

Root might fill some of the void that exists in agricultural extension.

It was actually launched earlier this year and has already “supported” more than 2,900 conversations about farm management, including troubleshooting for problems with machinery, FCC said.

AI gathers research

Being an AI tool, Root can gather information and learn from the latest agricultural results from research done in Canada and elsewhere.

“We are especially keen on incorporating RDAR (research) materials into Root … making our materials accessible to producers and ranchers,” said Mark Redmond, RDAR’s chief executive officer.

“We are pleased to formalize our partnership with FCC; in the past, we have worked on initiatives concurrently, but now we will collaborate more closely.”

For years, commodity groups for grains, oilseeds, pulses and livestock have used podcasts, webinars, YouTube videos, Twitter (X) and other technologies to share the best information with their members.

The new AI tool could be helpful for producers, but some extension experts still believe personal relationships matter.

Tracy Herbert, the knowledge mobilization and communication director with the Beef Cattle Research Council, said those modern tools can be effective, but personable relationships are critical when it comes to adoption of new agricultural practices.

“Without someone you have a trusted relationship with, who can provide that customized guidance… it’s far less likely that you’ll get to the last step in that process (adoption).”

The post Artificial intelligence put to work on extension appeared first on Grainews.