The latest ‘agri-AI’ tools can monitor animal welfare and crop health. But how much of a difference does the tech make, and how does it help buyers?

Fixed to a cattle chute at Agri-EPI’s South West Dairy Development Centre in Somerset, a small monitor is analysing cows’ legs and feet.

AI quote graphic

Using AI and thermal imaging technology, the ‘hoof monitor’ can spot lameness weeks before the farmer – saving£300 per cow typically incurred through treatment, reduced yield and shortened lifespan.

The early intervention is also thought to improve welfare and reduce carbon emissions per litre of milk produced.

It’s just one example of the agri-AI that’s hit the headlines in recent months – be it sensors detecting livestock disease, autonomous robots surveilling crops or monitors that count the wingbeats of bees.

But behind all the hype, to what extent does AI genuinely have the potential to transform agriculture? Are businesses embracing it? And how can retailers and suppliers get involved to speed up adoption and reap the benefits of more tech-savvy farmers?

Evolution of AI

Tech has been a fundamental tool on UK farms since the 1980s, when rudimentary sensors were used for climate control or to detect reproductive readiness in livestock.

These were followed by automated feeding systems, wearable collars to track individual animal metrics and, by the 2010s, remote monitoring software that tracked health and environmental conditions in real time.

Latest advances in AI – including its ability to collect, analyse and even make recommendations based on vast quantities of data – have unlocked a swathe of new potential applications and benefits. So much so that McKinsey estimates AI could eventually contribute $250bn to global agriculture via cost savings, improved yield and additional sales.

Scientists at the South West Dairy Development Centre Chris inspects grass

Source: South West Dairy Development Centre

Scientists at the South West Dairy Development Centre have worked on a huge number of emerging agricultural technologies

The applications in agriculture can be broadly divided into three core pillars: mitigating risk; driving efficiencies; and creating additional revenue streams.

The first area spans sophisticated monitoring tools such as the hoof monitor. These can detect disease in crops or livestock, or forecast climate events. The second area comprises tools that speed up production or reduce error, such as precision farming bots and autonomous tractors. Finally, we have the tools that likely do a bit of both, and indirectly create an additional revenue stream.

At Treefera, for example, its AI-enabled platform collates a vast amount of data from producers. That data is then ‘sold’ in exchange for carbon credits, explains Gabrielle Bourret-Sicotte, its ‘chief evangelist and head of customer success’.

“What theme ties all these use cases together?” asks James Watson, partner at consultants Argon & Co. “That AI is most effective when accounting for real-world variability such as unpredictable weather, changing soil conditions and shifts in grain moisture levels – all of which have long been the farmer’s enemy.

“AI in agriculture is about helping farmers make smarter, data-driven decisions – which, until now, hasn’t been possible.”

UK schemes designed to boost AI in agriculture

Adopt: Adopt is a new farmer-focused initiative funded by Defra and delivered by Innovate UK, which is set to launch in April 2025. Through a dedicated consortium, it will provide farmer-led, smaller-scale innovation grants for farmers, growers and farm businesses to trial new technology and methods on their farms. The Support Hub will also provide expert assistance to help farmers navigate the process, connect with facilitators and share knowledge.

BridgeAI: Launched in 2023, BridgeAI creates a £100m fund designed to drive AI innovation and encourage competitiveness in the UK. It has a focus on four sectors: construction; transportation; the creative industries; and agriculture. As well as funding, it also aims to bring together business, government, funders, research and the third sector to ‘bridge’ any divides over AI.

Farming Innovation Programme: Also overseen by Defra and Innovate UK, FIP makes funds available to farmers, growers and foresters who want to develop and use new, innovative methods and technologies, including AI, via thematic competitions.

Though the precise benefits vary wildly depending on the technology, it’s thought AI has the potential to reduce annual agricultural operating costs by more than a fifth (22%), according to Arkinvest.

“AI provides farmers with several benefits, including the ability to reduce overall inventory levels, minimising excess and obsolete stock, which helps prevent margin erosion and issues related to expired inventory,” says Tom Gregorchik, vice president for industry strategy at Blue Yonder.

“By optimising resource use and improving yield quality, AI contributes to increased profitability and sustainability in agriculture. Additionally, as farmers increasingly integrate AI into their operations, they can improve distribution, reduce waste and ensure that consumer demand is met. This can lead to improved productivity, less waste and enhanced profitability.”

That’s the business case on individual farms. But the benefits of sourcing from AI-equipped farms spans the entire supply chain. There’s the obvious shared upside of higher yields, lower costs and better-quality food on supermarket shelves – and much wider benefits are up for grabs, too.

To start with, AI equips farms with a level of real-time data on animal welfare and environmental conditions that even the most robust assurance scheme can’t match.

“If you’re a retailer relying on a farm assurance audit to tell you these animals are being looked after, they’re healthy and welfare is good, you’re basing that on one maybe two inspections per year,” says Johnny Mackey, associate director, stakeholder engagement lead for MSD Animal Health.

Inrow - front view

The RoboCrop AI in-row weeder is able to go into greater detail than previous tech when distinguishing weeds from crop plants

Assessors might show up on what looks like “a model farm” with gleaming power-washed facilities and “cows bedded up to their bellies in fresh straw” and tick off their checklists, he points out. But the data might tell a different story – of livestock lacking enough water or fighting to feed.

“These devices are looking at the animals constantly,” he adds. “It tells you the truth and doesn’t rely on subjectivity.” That gives retailers and suppliers substantial evidence to verify and support on-pack welfare claims. It’s a crucial benefit at a time when 84% of consumers say they’d be put off by any hint of ‘welfare washing’, according to 2024 research by The European Consumer Organisation.

Plus, as major suppliers face growing regulatory pressure to track and report on sustainability metrics – on welfare as well as wider environmental impacts like carbon emissions and deforestation – they’ll need the granular data AI offers to meet their obligations.

“Suppliers absolutely need our services,” says Bourret-Sicotte. “Everything is a synergy because the farmer is ticked off as, say, EUDR-compliant and then suppliers like Nestlé, Nespresso or Unilever are safe to use them.”

Herd Vision 1

The Herd Vision camera sits above a cattle chute and captures 3D moving images

A win-win?

If it’s all such a win-win, the industry should be falling over itself to get its hands on this innovation. Yet that’s not quite the case.

For some established technologies, like sensors on livestock, adoption is pretty widespread. MDA Health estimates about 50% of UK farms are using some type of monitoring, for example, though they vary in levels of sophistication.

But when it comes to the type of AI being splashed across headlines, there’s greater hesitation. Much of it isn’t commercially available yet, of course. Early trials are housed at research institutes, innovation hubs or demonstration farms, like the South West Dairy Development Centre.

Historically, there’s also been a tendency for AI to be developed by tech providers and academics,with little input from farms, says Chris Lyons, innovation lead for agriculture at Innovate UK.

“AI and machine learning is only as good as the data you feed into it”

Judith Batchelar, Food Matters International

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While that’s starting to change, a far bigger hurdle is price tag. “Five years ago, [a farm] would have had to hire AI scientists, data scientists and engineers, build their own models and train it on their ground truth data. That would cost millions – definitely not accessible for any farm, except the really large ones,” points out Bourret-Sicotte.

Those costs are coming down somewhat. “Now companies like ours provide AI models as a service, so we have all the risk and we’ve hired all the really smart people,” she says.

 

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Advances in Compute – roughly speaking, processing power – like faster processing cards from the likes of Nvidia “have made the cost of AI much lower”, Bourret-Sicotte adds.

But the reality is, early agricultural adopters of these technologies are likely to be shelling out quite a bit in some cases – no mean feat in an industry with notoriously low margins.

“Technology goes through economies of scale – ie it needs to get more products out there to reduce its unit price – but you’ve still got to be a risk taker if you’re going to go out there and be an early adopter,” says Lyons.

Agrisound 4

Source: Agrisound

AgriSound’s acoustic sensors detect insects to help provide data on biodiversity, quality and crop yields

That puts the tech largely out of reach for the UK’s roughly 30,000 small farms. “Many farmers, particularly smallholders, struggle to secure the capital needed for AI investment, and available financial support is often insufficient to scale up its use on a commercial level,” says Nandini Chakravorti, associate director for digital engineering at the Manufacturing Technology Centre.

“Regulations such as data protection laws, drone usage and food safety compliance further complicate AI adoption. Navigating these legal and operational requirements can be time-consuming and costly.”

Watson points to another hurdle. “Skills and training gaps can also slow the adoption of AI in agriculture, as farmers need practical training to interpret AI-generated recommendations and make informed decisions based on them. If AI tools lack user-friendly interfaces, they risk being ignored or misunderstood.”

Take ethical chocolate brand Luker. It equipped farmers with AI to improve analysis of cocoa bean fermentation – a vital stage in chocolate production that involves the pulp in the cocoa bean broken down by yeast and bacteria, giving it that all-important chocolate scent and taste. Despite the benefits, there was some hesitation across its Colombian supply base, admits director of digital transformation Daniel Basto.

“The biggest challenge was integrating it with our people and existing processes,” he says. “When we first introduced the AI-powered fermentation tool, it wasn’t widely adopted — mainly because those using it hadn’t been involved in shaping or training with it from the start.

“Without that understanding, the technology felt unfamiliar and there was a lack of trust in its ability to integrate into a well-established buying process.”

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This scepticism isn’t always misplaced, either. “There’s been some snake oil-type stuff going on, like in any new market,” says Casey Woodward, founder of AgriSound. Plus, there’s an attitude of “if it’s not broken, don’t fix it”. That’s less the case with younger farmers or more corporate groups, who can be more open to innovation, he says, but there’s a hesitation to spend money on what can feel like a risky new piece of kit, regardless of all the lofty promises.

“Many farmers struggle to secure the capital needed for AI investment”

Nandini Chakravorti, Manufacturing Technology Centre

The other challenge – at least, when it comes to unlocking the benefits in AI for the whole supply chain – is data readiness. So says Judith Batchelar, director of Food Matters International.

“The single biggest barrier is data standards,” she says. Take the monitoring of greenhouse gas emissions on a farm. Currently, the data collected and put into a carbon calculator lacks standardisation.

“AI and machine learning is only as good as the data you feed into it to learn on,” she says. “And if you don’t give it good, standardised data you can create all sorts of challenges – it can emphasise errors or inaccuracies.”

Get it right, though, and there’s huge potential. AI is now being used to analyse the nuances of geography, soil and microclimate to gauge the likely impact of biodiversity interventions at speed, for example. But again, there is emphasis that “you need to have good data to start with”.

The ‘farm of the future’: inside the South West Dairy Development Centre

Hoofcount footbath 2

Hoofcount’s Pedivue technology uses computer vision and machine learning to help reduce incidents of lameness

From AI-enabled cow collars to ‘virtual vets’ connected to farmers via AR and robotic milkers that monitor yield, a huge number of emerging technologies have been tested at the South West Dairy Development Centre since its opening in 2018.

Created as a partnership between dairy consultants Kingshay and the Agri-EPI Centre, a network of tech innovators and agricultural experts, the 180-cow dairy unit has been designed as a template for future farming and a testbed for all sorts of technologies that hope to revolutionise the sector.

Success stories include the likes of Hoofcount, a provider of automated footbaths for livestock. It wanted to develop a device that could proactively identify poor hoof health. So the company embarked on a two-year project to create Pedivue, a tool that retrofits to existing foot balls. It uses computer vision and machine learning to capture a clear view of an animal’s hoofs and identify lesions and other problems that can cause lameness.

Collaborative effort

The reality – for all of these reasons – is that AI likely won’t have the forecasted impact on agriculture unless retail and supply partners weigh in. That’s why, when AgriSound sought to get its bio-acoustic device implemented, it bypassed the farms and went straight to the retailer they supplied.

“The main value add for the corporates is they’ve then got that data they can use to feed into their sustainability reports,” says Woodward. “So, in most cases, either the retailer or the processor or integrator will purchase the technology or the licenses for it and deploy that into the supply chain.”

Marks & Spencer, for example, has implemented AgriSound across 10% of its supply base. “We’re a small team, so being able to go to someone who’s got that aggregation factor makes it more efficient for us,” Woodward adds. It also helps achieve “critical mass of interest” needed to scale the tech up.

“AI in agriculture is about helping farmers make data-driven decisions”

James Watson, Argon & Co

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At Treefera, too, the provider works in tandem with farmer-producers and their buyers. “We provide data to both of them, they work in tandem and we provide the transparency,” says Bourret-Sicotte.

For its part, MDA Health has advocated for a ‘fully connected industry framework’. That would combine more targeted regulation and policies, and a joined-up strategy for agricultural adoption across supply chains and market premiums. The idea is to incentivise take-up on farms and reflect the fact that retailers and suppliers are also reaping the rewards of AI and other technologies.

Permia

Permia’s acoustic sensors are sensitive enough to detect the red palm weevil with 97% accuracy

“What can never happen is a supermarket or whomever getting access to the data without putting some skin in the game and using that data to impose maybe penalties on their farmers,” says Mackey. “That’s absolutely what we don’t want to happen.

“The ideal scenario would be a partnership between the retailer, the processor supplying that retailer and the farmer supplying that processor and the technology provider,” says Mackey.

“And if the processor/retailer is putting some investment in there, that’s buying them a right to that data, isn’t it? That would be the understanding. The farmer’s getting a better deal on devices proven to help their business be more efficient and therefore more profitable. And the supply chain wins because they’re getting data that’s going to help them meet targets and verify their claims.

“It’s genuinely a win-win situation,” he sums up. The benefits of which arguably won’t be accessible if farmers are left to their own devices, though.

Five AI tools targeted at agriculture

Herd Vision 2

The Herd Vision system in action

Herd Vision

One of a bunch of remote AI-enabled monitoring tools designed to detect the smallest changes in animal behaviour as a way to identify changes in feed patterns, optimise reproduction and detect mobility issues.

The Herd Vision camera sits above a cattle chute and captures 3D moving images. Data is fed back via an intelligent online monitoring system and is delivered directly to a farmer’s table or smartphone. For a 200-cow herd with an average incidence of lameness that is required to record mobility four times a year, the benefits add up to over £11,000 per year, says the company.

AgriSound

Using specialised acoustic sensors, AgriSound’s technology can detect the sound and frequency of insects, such as bees and butterflies, in a particular area, transmit data via cellular or satellite connections and analyse it to provide farmers with insights on biodiversity, produce quality and crop yields. It’s secured some big industry backers, too. From 2023, M&S rolled out the technology across a number of its Select farms and in February Noble Foods began a pilot project using the tool on its Purely Organic farms.

Robocrop AI

First unveiled at the World Agri-Tech Innovation Summit in September, Robocrop AI is a precision technology tool designed to sit ‘in row’ and automatically distinguish weeds from crop plants. Crucially, while other models have relied purely on colour – ie, weeds are brown and plants are green – Robocrop uses AI to combine colour with infrared and depth information imaging to precisely identify crops versus weeds, even where the colour is ‘green on green’.

Permia Sensing

The red palm weevil costs the global palm industry billions through lost production and the costs of pest control and tree removal. But London-based Permia Sensing believes it has part of the solution. The agri-tech startup uses acoustic sensors sensitive enough to detect the beetles with 97% accuracy, allowing farmers to act preventatively before they multiply or spread to nearby trees. The technology is currently being trialled on plantations in Sri Lanka.

Fermata

Fermata

Source: Fermata

Rather than growers spending hours surveilling crops, Fermata’s ‘Croptimus’ stands watch instead, using advanced machine learning and computer vision to detect pests and disease on crops and alerting them via a dashboard. It can also analyse patterns over time, says the company, to spot any underlying cause that might be creating persistent problems. Fermata says this early detection can lead to an up to 50% reduction in time spent scouting, 30% reduction in crop loss, and 25% reduction in crop inputs.