Canada moves to ban under-16s from social media, regulate AI

AFP
June 10, 2026 

Canada’s culture minister Marc Miller argued the new legislation would protect children from online harms – Copyright AFP Lars Hagberg

Canada’s culture minister on Wednesday introduced legislation that would ban children under 16 from having social media accounts and require AI chatbot services to limit production of harmful content.

The proposed Digital Safety Act makes Canada the latest in a wave of countries cracking down on social media platforms over concerns of harm to children.

“We have seen the very serious consequences that online harms can have…The safety of children cannot be an afterthought,” said Culture Minister Marc Miller in a statement announcing the proposal.

The legislation would ban social media accounts for children under 16 years old, the statement said, adding that there be an exemption “pathway” for companies if they can demonstrate “sufficient safeguards” for children.

Social media services, including adult content platforms, would also face new requirements under the law to “mitigate risks associated with exposure” to various categories of harmful content and apply labels to synthetically generated content.

The eventual regulations would be enforced by a Digital Safety Commission, with possible fines on companies not in compliance of up to three percent of their global revenue or CAD$10 million.

“This legislation will provide a safer environment for young Canadians and empower them to connect in-person, build friendships, focus in school, and learn real-world skills so they can thrive,” Health Minister Marjorie Michel said in a statement.

Sachin Maharaj, an education professor at University of Ottawa, called the proposal “a step towards the right direction,” with a “recognition that social media is associated with behavioral and social issues.”

“Obviously, kids will find their way around” restrictions, he told AFP. “But the real challenge is to change the way the apps work.”
AI rules

In addition to the social media ban, the new law would also regulate increasingly ubiquitous AI chatbots by requiring companies to “mitigate the risk of the chatbot communicating harmful content.”

Companies would also face requirements for transparency around “reporting thresholds in crisis situations,” such as when a user intends to harm themselves or another person.

The issue has been particularly sensitive in Canada following a mass shooting in April that left nine people dead in the small mining town of Tumbler Ridge, including the shooter.

OpenAI has faced intense criticism after it banned the shooter from its platform in June last year over the user’s troubling conversations on ChatGPT, but did not report the account to Canadian police because it said it saw no evidence of an imminent attack.

In December, Australia became the first country in the world to require TikTok, YouTube, Snapchat and other top sites to remove accounts held by under-16s or face heavy fines.

Indonesia began enforcing its own social media ban for users under the age of 16 in March, while several European governments have announced their desire to make similar moves.

 How would Canada’s social media ban work? Platforms, apps not sure

By

The Canadian Press

Opens in new window

Published: June 11, 2026 

A teenage girl uses her phone to access social media in Sydney, Friday, Nov. 8, 2024. (AP Photo/Rick Rycroft)

As countries including Canada move toward social media bans in an attempt to keep youths safe online, tech companies are in a tug of war over who should be the gatekeepers.

Executives from Snapchat and Meta, the owner of Instagram, Facebook and Threads, have argued it should be app stores rather than platforms charged with verifying the ages of users when they try to add a platform to their phones.

Apple and Google, which run the App Store and Play Store respectively, have introduced some age-gating measures but appear to be at odds with social media platforms over whose responsibility those measures should be.

Experts say trying to put the onus on either side is pointless because app store owners, platforms, governments and parents all have to step up to keep kids safe online.

“I don’t think it’s fair for any company to say, ‘We’re just the host, it’s their problem,’” said Kaitlynn Mendes, a sociology professor at Western University and the Canada Research Chair in inequality and gender.

“It’s all of our problem.”

The battle over who should police youth access to platforms has been simmering for years while child safety advocates called for help addressing the evolving harms young people are encountering online.

The tension hit a crest late last year, when Australia banned anyone under the age of 16 from using social media. A study has since suggested 70 per cent of the country’s Instagram, Snapchat and TikTok users under 16 have held onto their accounts despite the crackdown.

The United Kingdom, France, Poland, Indonesia and now Canada are among the countries taking similar action to Australia.

The federal government announced Wednesday that social media companies will eventually have to block access to their platforms to Canadian users under 16 unless they follow unspecified safeguards.

Social media companies largely oppose youth bans, but say if they are implemented then app stores should do the heavy lifting rather than each individual app.

“We’ve advocated for age verification by app stores instead of individual apps — not because we support under-16 bans, but because if this policy exists it needs to have uniform implementation that safeguards privacy and security for users,” Snapchat CEO Evan Spiegel said in February opinion piece published in the Financial Times.

His sentiments were echoed by Meta, which has been pushing the Canadian government to implement age verification at the app store level since at least last year.

Google doesn’t like that pitch.

Kareem Ghanem, senior director of government affairs and public policy at Google, has framed it as part of a consistent pattern of “advancing policy proposals that demonstrate more interest in shifting responsibility than in taking responsibility.”

“Time and time again, all over the world, you’ve seen them push forward proposals that would have app stores change their practices and do something new without any change by Meta,” he said in November.

Earlier this week, Apple introduced some new features to its child accounts, which can limit access to adult websites and set age-based restrictions for the App Store. Child accounts are required for children under 13 and available for children up to 18.

Apple CEO Tim Cook attributed the move to Australia’s social media ban.

Mendes said that some companies try to weasel out of taking responsibility for administering bans by pointing the finger at others because they’re difficult to enforce.

Kids are “incredibly crafty,” she said. In Australia, she said they’re using technology that obscures their location to maintain accounts, having older siblings or even parents open accounts for them or turning to other apps that aren’t included in bans.

“Kids are way more motivated to find ways to bypass them and then ... we end up spending our time policing children, whereas I think that energy is better spent forcing the companies to design products that are safe,” Mendes said.

The difficulty in enforcing bans is why both app stores and platforms have to step up, said Jenna Poste, the national technology impact adviser at Unplugged Canada, a grassroots organization encouraging parents to consider delaying their child’s access to phones and social media.

App stores alone can’t age-gate because kids can still access social media on browsers or non-Google and Apple devices, Poste said. Kids could use a web browser instead, for example.

This report by The Canadian Press was first published June 11, 2026.

Tara Deschamps, The Canadian Press

Canada Faces Surge In Social Anxiety

 June 10, 2026 

By Eurasia Review

A new Canadian study has found that social anxiety disorder (SAD) now affects nearly 1 in 7 adults — a 71% increase since 2002 — making it one of the most common mental health challenges in the country.

Social anxiety disorder, sometimes referred to as social phobia, is a mental health condition characterized by an intense fear of being judged or embarrassed in social situations, often leading to significant distress, impaired relationships and work performance, reduced quality of life, and substantial economic costs to society. “Social anxiety is becoming more common in Canada, and understanding why this increase is happening is essential for improving mental health support,” said Tak-Lai Nellie Chau, recent MSW graduate of the Factor-Inwentash Faculty of Social Work (FIFSW), University of Toronto.

The research revealed that nearly 14% of Canadian adults have experienced social anxiety at some point in their lives, up from just over 8% in 2002.

While the study did not examine why social anxiety may have increased, the authors hypothesize that recent social changes, such as increased use of social media and the effects of the COVID-19 pandemic, may be contributing to this rise

Young People Are Most at Risk

The research shows a strong pattern: younger Canadians are far more likely to experience social anxiety than older adults. About 1 in 4 or 24% of people aged 20 to 24 had a social anxiety disorder at some point in their life, compared to only 6.2% of those aged 65 and older.

Digital communication and reduced face-to-face interaction could be to blame.

“Young adulthood is a key life stage where social pressures are high, which may increase vulnerability to social anxiety,” said Stephen A. Oliver, recent MSW graduate of the FIFSW, University of Toronto. “Add to this the experience of increased isolation during the pandemic, growing pressures to achieve certain ideals on social media, and increased polarization and those pressures are bound to  intensify.”

Early Life Experiences and Childhood Trauma Play a Role

The study also found that early life experiences are strongly associated with social anxiety disorders.  Individuals who experienced childhood sexual abuse or witnessed domestic violence had significantly higher rates of social anxiety disorder.

The prevalence of lifetime social anxiety was also linked to chronic pain, multiple health conditions, and substance use disorders, showing that social anxiety often overlaps with other challenges.

“These findings highlight the importance of a life course approach to understanding mental health,” said Esme Fuller-Thomson, Director of the Institute for Life Course and Aging, and Professor at the FIFSW, University of Toronto. “Childhood experiences can cast a long shadow on adult mental health”.

Social Support Makes a Difference

The research showed that people with low social support — those who felt they didn’t have someone to rely on — were much more likely to have a social anxiety disorder.

At the same time, people who reported that spirituality was important in their lives were less likely to struggle with social anxiety.

“While our study did not test treatments directly, there is strong evidence from other research that cognitive behavioural therapy can be highly effective for social anxiety. Given that one in four young Canadians are affected, improving access to these evidence-based supports is essential,” said Fuller-Thomson.

What is ‘doomjobbing’? Here’s why experts are concerned about this trend

By

Christl Dabu

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Published: June 09, 2026 

Experts say "doomjobbing" isn't a good strategy for finding work. (Pexels)

Many people desperate to find work have been “doomjobbing,” sending mass job applications with the help of artificial intelligence and inundating hiring teams, according to employment insiders.

Doomjobbing highlights some of the challenges facing jobseekers amid a fiercely competitive job market and rising youth unemployment rate, experts say, but it isn’t the best strategy.

Alexandra Tillo, senior talent marketing consultant at the jobs site Indeed, said applying to dozens of jobs in one sitting may “feel productive,” but it can backfire and lead to burnout.

“The challenge is that the quantity doesn’t always translate into results,” she said in a Zoom interview with CTVNews.ca from Toronto on Monday. “And so when you have candidates that spend very little time tailoring applications or even thinking critically about the fit for the role, they often end up putting more effort while they’re seeing fewer (positive) outcomes ...”

What is doomjobbing?

Doomjobbing means “endlessly applying to a high volume of jobs with relatively low energy, low focus, and low personalization or detail,” Ben Shepherd, branch director with the global employment agency Robert Half, said in a Zoom interview with CTVNews.ca from Calgary on Monday. 

Doomjobbing has been likened to the job search version of “doomscrolling.” Doomscrolling is already found in dictionaries, which define it as spending a lot of time reading negative news online.

Doomjobbing has now been gaining attention online. In an Instagram post on May 22, Cambridge Dictionary asked if doomjobbing should be a new word.

Why doomjobbing?

Generative AI has contributed to the rise of doomjobbing, Shepherd said. People have been using AI to make it more efficient to apply for multiple jobs, he said, bombarding job postings across different industries with automated job applications.

Doomjobbing is coming from pressure, not laziness, said Nancy D’Onofrio, vice-president of strategic accounts and social selling with recruitment firm Randstad Canada.

“So there’s a lot of job market anxiety, and technology in the midst of that really makes it easy to submit, right?” D’Onofrio said in a Zoom interview with CTVNews.ca from Toronto on Monday, noting people can get into a “reactive mode instead of a proactive strategic mindset when applying.”

Doomjobbing could result from a “confidence gap,” D’Onofrio added.

A 2026 report from Randstad found that more than nine in 10 employers are confident in the growth of their business this year, but just over half of workers feel the same.

This “confidence gap” is based on insights from more than 27,000 workers, 1,225 employers, and more than three million global job postings in 35 markets across Europe, Asia-Pacific and the Americas, including Canada.

The survey, which was conducted in October 2025, also found that the workplace is marked by “economic strain” on both staff and employers as AI increasingly is adopted.

“So based on our research and the fact that there’s a confidence gap, talent just feel like they have to apply more, and volume is the game that obviously they’re leveraging,” D’Onofrio said.

Hiring teams’ concerns

While AI can be a helpful tool, it can hinder applications if “misused or overused,” Shepherd added.

“Not also only the high volume, but (employers are) having to spend more time qualifying these candidates because everyone looks the same, having utilized a lot of the same gen AI prompts to generate their resumes,” Shepherd said.

According to a survey published by Robert Half in March, 64 per cent of hiring managers said the surge in applications and concerns about the authenticity of AI-generated resumes pose challenges for their companies. It featured responses from 1,500 hiring managers across Canada.

How to stand out

Tillo of Indeed said candidates can often see “stronger results” if they shift their focus “from volume to relevance,” with fewer and higher quality applications.

“Don’t mass apply ... Ask yourself if the role actually works for you,” she said. “You can look at the location, the schedule, compensation, if it’s a remote job, if it’s in person, if it’s a long-term fit.”

D’Onofrio said jobseekers should leverage AI as a “strategic tool” to obtain insights about a company, which could help them tailor their applications. AI can be used to research a company, understand its challenges and how candidates could add value, including where their skills may have the most impact, she said as examples.

Beyond AI, candidates also can reach out to hiring managers, such as through social networks like LinkedIn, she added.

“And it’s very important to figure out, how can I still have my voice shine through, and that’s what talent acquisition individuals are looking for,” D’Onofrio said. “It’s that human attribute, it’s that human skill, it’s that connection that they’re looking to build with that talent, and that’s what’s going to set you apart.”

Christl Dabu

Opens in new window

National Affairs Writer, CTVNews.ca

The Indian workers training AI robots to take their jobs

AFP
June 11, 2026 

Indian housewife Nagireddy Sriramyachandra wears a smartphone on her head as she records her actions through motion capture while slicing mangoes at her home in Chennai – Copyright AFP R.Satish BABU


With a smartphone strapped to her head, Indian housewife Nagireddy Sriramyachandra films herself slicing mangoes to train AI-powered robots to take on household jobs in the future.

Earning just over two dollars for an hour of video, her mundane recordings are invaluable for global tech companies teaching machines how to move like humans in the real world.

The 25-year-old is one of a growing army of thousands of AI system trainers in the world’s most populous country.

“Who else will give you 250 rupees an hour just for doing housework?” said Sriramyachandra from her kitchen in Chennai in southern India’s Tamil Nadu state.

“I may get a robot myself in the future,” she added.

Artificial intelligence chatbots and image generators crunch reams of digital data, but building systems to navigate real-life environments is more challenging.

Developers think feeding first-person footage, called “egocentric data”, into specialised AI models will help robots copy humans.

Some AI trainers work at home, others in factories or specialised studios — using video glasses, head-mounted cameras and motion sensors.

“It blares ‘hands not detected’ when I’m not recording properly,” said Sriramyachandra, who sends recordings via a special app to the AI data company Objectways.

The firm, which has offices in India and the United States, lists Fortune 500 multinationals as clients. It works with Amazon SageMaker, a platform for machine learning models.



– ‘Better things’ –



The humanoid robot market is booming, with investment bank Morgan Stanley predicting there could be over a billion in use by 2050, mostly for industrial and commercial purposes.

“Folding clothes, coffee making… cooking a very specific thing, sandwich making,” Objectways head Ravi Shankar said, listing videos requested by clients.

“Some jobs are supposed to be taken over, so humans can go and do better things.”

In India, the emerging field of spatial AI is providing new employment — for now.

The 50-year-old CEO is US-based, but hires workers from Tamil Nadu, where he grew up, one of India’s international technology hubs.

At a Karur textile factory, busy with workers attaching labels to caps and ironing cloth bags, AFP saw eight people wearing head cameras and smart glasses supplied by Objectways.

India has positioned itself as a global middleman for the creation, processing and annotation of AI data.

“It’s likely that these data collection services will increase”, said digital labour expert Aditi Surie, from the Indian Institute for Human Settlements in Bengaluru.



– Informal workers –



India is aggressively developing its AI industry, but its leaders are aware that, alongside the technology’s much-hyped benefits, automation poses risks.

Government think-tank NITI Aayog said that most discussions around artificial intelligence and labour “focus on white-collar professionals and predict an almost certain loss of jobs in the segment” without urgent action.

“Little attention, if any, is paid to how AI can serve India’s 490 million informal workers, the very people who form the backbone of our economy,” it said in a report released ahead of a global AI summit in India this year.

The think-tank has examined how the technology could help or harm dozens of professions — from cobblers to sewer cleaners, farmers to tea sellers.

For the last decade, 55-year-old Ponni has sat on a roadside in Bengaluru, the city known as India’s Silicon Valley, making flower garlands.

She, too, has been paid to have a phone strapped to her forehead.

“The next generation… who might have to do work similar to mine — they will face a problem,” Ponni said.



– Always wearing a camera –



At an Objectways studio, AI system trainers film themselves performing household tasks in fake, fully furnished apartment rooms.

After several thousand hours of filming, the wallpaper is changed to provide clients with variety.

“Today I sit here, tomorrow I stand there,” said engineering graduate Rani N., 21, on a break from filming herself, once again, folding a towel.

Each video lasts about four minutes, and she records around 90 a day — on nearly every conceivable spot on the bed.

She says the job is “tolerable”, but feels like she’s always wearing a camera.

In other rooms, colleagues arranged pencil sharpeners, water bottles and crayons in patterns, recording with depth-sensor cameras.

Qanat Consulting Services in Andhra Pradesh, an Objectways subcontractor, supplies about a dozen larger data firms with recordings.

Some of its 2,000 contributors perform tasks with motion-sensor bands on their “wrists, hands and legs”, CEO Thaslim Pattan said.

Manish Agarwal of Bengaluru-based Humyn Labs, not related to Objectways, records conversations as well as videos.

Contributors discuss assigned topics — ranging from politics to entertainment — for clients wanting to process speech patterns.

Agarwal denies that robots will steal jobs, believing that networks of humans and robots “will work together” one day, he said.

“A welder in India could be managing a welder-robot in Prague,” he said.

Struggling German auto supplier Bosch pivots to robots

AFP
June 10, 2026

Bosch says the market for its sensors that are used in robots is growing rapidly – Copyright AFP/File RONALDO SCHEMIDT

German industrial giant Bosch said Wednesday it will step up efforts in the field of humanoid robotics, as its traditional auto parts business comes under increasing pressure.

The world’s biggest auto supplier, Bosch makes everything from braking systems to sensors, but has suffered as European carmakers battle fierce overseas competition and weak demand.

However, the rise of humanoid robots, powered with generative AI models and capable of performing complex tasks, offers an opening for the group, chief executive Stefan Hartung said.

“With the advent of humanoid robotics, the demand for Bosch components and solutions is increasing,” he said in a statement.

The market for specialised MEMS sensors is expected to grow to over $19.2 billion by 2030 and hit an annual growth rate of four percent, according a study by consultancy Yole Group, which was presented by Bosch.

Bosch is a key producer of the tiny sensors, which are crucial in robotics.

At an event in Berlin, Hartung stressed the importance of the components in improving the dexterity of robots.

These sensors determine whether the robot “should tighten its grip or not, whether it is dealing with a sturdy object, or whether it needs to act delicately because it is an egg,” he said.

“Humans have four million touch sensors. If we were to build robots equipped with as many sensors, four years of global sensor production would barely be enough to equip 12,500 robots,” he added.

The focus on automation is also meant to boost the competitiveness of Bosch’s German factories and plug shortages of skilled labour.

Bosch, also known for making a wide range of industrial equipment and household appliances, struck a deal with German robotics firm Neura in January to gather data on factory work.

Under the partnership, several thousand workers in some of Bosch’s 350 facilities worldwide will wear sensor suits to glean training data for Neura robots.

Neura said Wednesday it had raised up to $1.4 billion in fresh fundraising from backers including Bosch, chip giant Nvidia, Amazon and crypto group Tether.

The funding will be used to accelerate Neura’s activities, ranging from the deployment of robots to the rollout of “gyms” for clients to train bots, and the development of the company’s physical AI systems.

The factory floor’s last manual process gets an AI upgrade

Jon Stojan

DIGITAL JOURNAL
June 10, 2026 

Photo courtesy of Envato.

Opinions expressed by Digital Journal contributors are their own.

Inside an RV plant in northern Indiana, a curved fiberglass countertop that used to take an hour to hand-finish now leaves the line in six minutes. Sanding and polishing a complex surface to a uniform sheen had long resisted autonomous finishing, and for a counterintuitive reason: the variation inherent in geometry and surface condition made it the hardest job on the floor to teach a machine. By 2023, more than four million industrial robots were operating on factory floors worldwide, according to the International Federation of Robotics’ World Robotics report. While CNC machining handled the cuts and robotic assembly handled the bolts, surface finishing held out longer than either of them.

The floor’s last rule: Hands only

Every autonomous process on a factory floor follows a predetermined path:

A weld traces the seam an engineer drew
A mill follows the tool path a CAM system generated
Even bin-picking, long the benchmark problem for difficult autonomous applications, became tractable once vision systems learned depth perception

Surface finishing has no such path. Part geometry and material thickness vary. The surface flaws on any given part, such as a small ridge left by a mold or a soft spot in the gel coat, also vary from piece to piece. A skilled finishing operator senses pressure through the wrist, hears the tone of the abrasive shift, watches resin dust change color as it heats, and adjusts continuously. That kind of real-time adaptive judgment can’t be reduced to a routine written months earlier in an engineering office.

GrayMatter Robotics, a Physical AI company building Factory SuperIntelligence (FSI) for manufacturing, deploys autonomous finishing cells designed for exactly that constraint. Where software AI systems learn from internet data, Physical AI systems operate in and learn from the physical world. GrayMatter Robotics’ autonomous finishing cells draw on ATLAS, the company’s proprietary data regime comprising 7 petabytes of real-world surface finishing data accumulated across 30 million square feet, 20-plus industries and 11-plus sensing modalities. That foundation develops Process Intelligence, the learned understanding of how tools, materials and surfaces interact under real manufacturing conditions, enabling the system to adapt in real time to whatever geometry and surface condition the part presents, without pre-programming. The result is closer to what a craftsperson does than to what a conventional robot does.

The craft behind the calluses

A traditional finishing apprenticeship begins with feel. The early months are less about technique than about calibration: learning how the body interprets pressure and how the same motion produces different results under different conditions. Apprentices learn abrasive selection and progression. They learn how a given resin responds to heat and how ambient humidity affects how a coating lays. That education has genuine value, but it requires four to six months to reach a productive level and years to reach mastery. In a labor market where manufacturing competes against every sector for the same generation of workers, that timeline is increasingly difficult to sustain.

“Surface finishing has always been treated as an art, something you learn through years of practice. But it is physics, and once you model it correctly, you can build systems that learn and adapt in ways that traditional robots can’t,” said Ariyan Kabir, Co-Founder & CEO of GrayMatter Robotics. “The breakthrough for us came when we realized that the skill operators develop over years is really their internalized understanding of physics in action. Encode that physics in software and you can deploy that capability anywhere.”

Autonomous finishing cells change what operator training needs to produce. Workers who once required deep manual craft now need systems fluency, the understanding of what the machine is doing and why, rather than the physical capacity to replicate it.

Five jobs gone, or five jobs changed?

The arithmetic that initially looks like subtraction often resolves differently. When a single autonomous cell handles the work that previously occupied six finishers, the instinct is to read that as five positions eliminated. In practice, facilities that deploy these systems tend to expand rather than contract. The throughput gains that justify autonomous finishing are the same gains that make it possible to take on volumes previously out of reach. Two or three cells running in parallel require upstream support and operational oversight the prior headcount wasn’t providing. The floor is busier, not quieter, and the roles shift accordingly.

A second force is accelerating this transition. CAD and CAM tools have made it cheaper to design parts with curves and undercuts that an engineer would have avoided a decade ago because no one could finish them consistently at volume. Research published in Materials found that conventional CNC machining strategies relying on fixed step sizes are inherently inefficient for surfaces with rapidly varying curvature and that aligning tool paths to local surface geometry reduced form error by 48.4% in a single pass.

Facilities still relying on manual finishing are quietly discovering that the geometry coming out of engineering has outpaced what a human finisher can produce consistently at scale. Geometry-agnostic autonomous systems convert that pressure into a competitive advantage. The complex surface that once represented a bottleneck becomes another part moving through the queue.

FAQs

1. Why has surface finishing resisted autonomous solutions longer than other manufacturing processes?

Every other major manufacturing process follows a predetermined path, but surface finishing doesn’t. Geometry variation is the primary factor: no two parts present exactly the same surface, and materials behave differently under heat and pressure. Surface flaws vary part to part, and pre-programmed systems that execute fixed instructions lack the continuous adaptive judgment that finishing requires.

2. How do vision systems enable robots to adapt to different part geometries?

Vision systems allow autonomous finishing cells to read each part’s actual geometry in real time rather than executing a path written to a CAD model. Because part dimensions vary within normal manufacturing tolerances, a system that adapts to what is physically present rather than what the drawing specifies handles geometry variation and surface irregularities without manual reprogramming between parts.

3. What integration challenges should manufacturers expect when adding autonomous finishing to existing production lines?

The most common issues involve floor space allocation, electrical and pneumatic capacity, and workflow sequencing. Autonomous finishing cells are designed as standalone stations that fit into existing layouts without requiring line redesigns. Most facilities run autonomous and manual processes in parallel during an initial validation period before transitioning fully.

Thinking before picking: Smarter harvest robots and the impact on modern farming

Dr. Tim Sandle

DIGITAL JOURNAL

June 6, 2026

Sprinklers water a lettuce field in California’s Imperial Valley, a vital part of America’s huge agricultural sector, in February 2023 – Copyright AFP FOCKE STRANGMANN

Robotic harvesting has long been positioned as a solution to one of agriculture’s most persistent challenges, which is labour shortages. But while automation has advanced rapidly in areas such as seeding, spraying, and monitoring, harvesting delicate crops has remained a stubborn frontier. Tomatoes exemplify the problem. They grow in dense clusters, ripen unevenly, and are easily damaged, making them difficult targets for machines that rely on simple detection and repetitive motion.

A new research effort from Osaka Metropolitan University suggests a shift in approach. Rather than asking whether a robot can identify and pick a tomato, the system evaluates how easy it will be to harvest each fruit before attempting the task. This change, from detection to decision-making, signals a broader evolution in agricultural robotics, one that aligns more closely with the realities of commercial farming.

Most harvesting robots today are built around visual recognition systems. Cameras and machine learning models identify ripe fruit, then robotic arms attempt to pick it. The limitation is that identification alone does not account for the physical complexity of the plant environment. A tomato may be fully ripe but partially hidden, blocked by stems, or positioned in a way that increases the risk of damage.

Assistant Professor Takuya Fujinaga’s system introduces what can be described as a “harvest probability” model. The robot analyses multiple variables, variations like fruit position, stem orientation, surrounding leaves, and occlusion, before calculating the likelihood of a successful pick. It then selects the approach angle most likely to succeed.

In testing, this method delivered an 81% success rate, with a notable proportion of successful picks coming after the robot adjusted its strategy mid-task. When a frontal approach failed, the system recalculated and attempted a side-angle harvest, demonstrating a level of adaptive behaviour that has been largely absent from earlier systems. For agricultural operators, this represents a move toward machines that behave less like automated tools and more like semi-autonomous workers capable of situational decision-making.

Why this matters for commercial farming:

Harvest efficiency is not simply about speed; it is about yield quality, labour substitution, and operational continuity. Failed picks can damage fruit, slow down processes, and reduce overall productivity. By introducing a system that evaluates how likely a task is to succeed before execution, the research addresses several operational constraints:Reduced damage rates, as difficult picks can be avoided or approached differently
Higher overall throughput, as time is not wasted on low-probability attempts
Improved resource allocation, allowing robots to focus on high-yield tasks

This approach aligns with how human pickers operate. Experienced workers instinctively assess whether a tomato can be picked cleanly and adjust their movements accordingly. Translating this judgement into machine logic is a significant step toward closing the gap between human and robotic performance.

Canadian agriculture: early signals of a similar shift

While the specific “harvest-ease” framework is emerging from Japan, Canadian agriculture has already been moving in a similar direction, particularly in greenhouse operations.

In Ontario and British Columbia, greenhouse tomato producers have invested heavily in automation, driven by labour shortages and rising costs. Companies such as Nature Fresh Farms and SunSelect Produce have adopted advanced environmental controls, robotics, and AI-based crop monitoring. Although most harvesting in these facilities is still human-led, there is growing experimentation with robotic assistance systems.

Canadian agricultural technology firms have also begun to focus on decision-support rather than pure automation. For example startups in Ontario have developed vision systems that assess fruit ripeness, orientation, and picking readiness, feeding this information to human workers or semi-automated tools. With a different development, robotics developers in Quebec have explored adaptive gripping systems that adjust force and angle based on fruit characteristics. Furthermore, greenhouse operators are trialling AI-driven crop analytics to prioritise which sections of a crop should be harvested first

These efforts reflect a broader trend: automation in agriculture is shifting away from rigid, pre-programmed actions toward systems that evaluate conditions and adapt in real time.

Human-robot collaboration, not replacement

One of the more practical implications of the research is the model of shared workload it enables. Rather than replacing human labour entirely, the system supports a division of effort. Here, robots handle straightforward, high-probability picks and human workers focus on complex, delicate, or obstructed fruit.

This hybrid model is already emerging in Canadian greenhouses, where labour shortages have made full staffing difficult. In British Columbia’s Lower Mainland, growers have reported challenges in maintaining consistent harvest teams, leading to interest in technologies that can stabilise output rather than eliminate labour altogether. A robot that can filter out easy tasks and leave only the more complex work for humans reshapes productivity. Instead of matching human performance across all tasks, robots can contribute where they are most effective, improving overall efficiency without requiring full autonomy.

A critical next step for systems like Fujinaga’s is moving beyond controlled test environments into real farms, where variability is the norm. Factors such as changing light conditions, plant density, humidity, and unexpected obstructions introduce layers of complexity.

Canadian greenhouse operations, with their controlled yet dynamic environments, may provide an ideal testing ground. These facilities already maintain detailed environmental data and standardised crop layouts, making them suitable for integrating adaptive robotic systems. Field agriculture presents a greater challenge. Outdoor conditions introduce variability that requires even more sophisticated decision-making. However, the principle of assessing task difficulty before execution remains applicable across both settings.

Design implications for agricultural technology

The concept of “harvest-ease estimation” has broader implications for how agricultural robots are designed. This leads to systems built to prioritise tasks based on probability of success, rather than attempting all tasks equally. It is similarly important that robots adjust their approach dynamically, rather than relying on fixed movement patterns. These forms of decision-making models can be combined with crop monitoring systems to optimise harvesting schedules.

The idea of a robot “thinking before acting” may seem incremental, but it marks a shift in how automation is conceptualised in agriculture. Instead of focusing solely on mechanical capability or visual recognition, the emphasis moves toward decision-making under uncertainty. For Canadian farmers, particularly those operating in high-value greenhouse sectors, this aligns with ongoing efforts to modernise production without compromising quality. As automation becomes more adaptive, the barrier to adoption may lower, opening the door to wider deployment across farms of varying sizes.

In practical terms, this approach supports more efficient harvesting, reduced waste, and a clearer path toward collaboration between human workers and machines. For an industry managing labour constraints, cost pressures, and the need for consistent output, the ability to prioritise and adapt may prove as important as the ability to automate itself.