The demand for food worldwide is climbing rapidly, with experts estimating that agricultural production must rise by 70% within the next 25 years to sustain a growing population. Simultaneously, farmers are contending with increasingly unpredictable weather conditions and a pressing need for sustainable practices. The challenge is clear: how can the agricultural sector boost productivity while using fewer resources and minimising environmental harm? Artificial intelligence offers a compelling solution. While AI has been present in agriculture for some time, such as through auto-steering guidance systems, recent advancements in machine learning are accelerating its impact. By 2028, the AI agriculture market is expected to reach $4.7 billion, as more farmers seek smarter, more efficient ways to operate.
However, the adoption of AI technology doesn’t come without hurdles. It requires a significant upfront investment, and many businesses find the prospect of integrating new AI-powered solutions daunting. To address this, the UK government recently announced a £7 million initiative aimed at supporting small businesses in leveraging AI for productivity gains.
This article will highlight how agricultural businesses can maximise AI’s potential while mitigating risk. We’ll explore real-world applications, strategies for successful AI integration, and what the future holds for AI in agriculture.
Implementing AI in Agriculture
AI is transforming farming, providing more efficient solutions to longstanding challenges across the agricultural supply chain.
Crop phenotyping
One of the most promising AI applications in farming is the analysis of plant traits to optimise crop breeding. Traditional phenotyping has been labour-intensive and dependent on human observation, but AI is revolutionising this process. By scanning fields and detecting key characteristics, AI enables farmers to identify the best-performing plants with remarkable speed and accuracy. This technology has already demonstrated its potential – for example, in reducing the growth cycle of broccoli by two-thirds.
Precision agriculture
Drones, sensors, and GPS-guided machinery powered by AI have refined precision agriculture. Rather than treating entire fields uniformly, farmers can now pinpoint specific areas requiring more irrigation or pest control, leading to reduced resource consumption and improved efficiency. This not only lowers costs but also supports sustainability by reducing chemical runoff and conserving water.
Pest and disease detection
Previously, identifying plant diseases relied on time-consuming manual inspections. Now, farmers can use AI smartphone apps to analyse crop images and instantly detect diseases or pest infestations. Early diagnosis helps farmers take proactive measures, minimising crop losses and improving yield.
Livestock monitoring
AI is playing a crucial role in monitoring livestock health, tracking factors such as food intake and early signs of illness. Wearable sensors can detect shifts in movement or body temperature, alerting farmers to potential health concerns before they escalate. In dairy farming, AI can even predict the onset of infections like mastitis by analysing milking data.
Autonomous farming equipment
Labour shortages remain a major challenge in agriculture. As the average age of farmers increases and fewer young people enter the industry, AI-driven automation is stepping in. While drones and GPS-guided tractors are already widely used, robots are now being deployed to sort crops, identify defects, and even plant seeds – tasks that previously required extensive human labour.
Real-world AI Applications in Farming
AI is already being implemented successfully across the UK and beyond, solving agricultural challenges and shaping the future of food production.
In crop science, a partnership between Bayer and NIAB is using AI-driven phenotyping and multi-spectral imaging to assess plant health and nutrient levels, allowing farmers to enhance crop yields with greater precision.
John Deere has integrated AI to analyse environmental conditions, detect pests, and predict yields, providing data-driven insights that help large-scale farms optimise production.
Harper Adams University has developed an AI-powered dairy system that monitors cows individually, tracking their feeding habits and identifying early signs of illness to improve overall herd health.
At Tiptree’s strawberry farm, robots are being tested for automated fruit picking, meaning crops are harvested at peak ripeness with minimal waste.
Better Origin has created an AI-driven insect farm where black soldier fly larvae convert food waste into high-protein poultry feed, helping to reduce agricultural waste.
And finally, universities are now beginning to offer courses specially dedicated to upskilling for agricultural AI. For instance, the Royal Agricultural University in the UK has introduced an MSc programme in Agricultural Technology and Innovation, ensuring future farmers and industry leaders are equipped with AI expertise.
Key Strategies for AI Adoption in Agriculture
Invest in training and education
AI is only as effective as the people managing it. As automation and machine learning become more integral to farming, there is a growing need for skilled professionals. Many agricultural workers currently lack training in AI applications and data analysis.
Governments, universities, and private organisations are increasingly offering courses on AI-driven agriculture. Farmers should leverage these educational opportunities to gain practical knowledge on integrating AI into their operations. Additionally, businesses should provide ongoing support and technical assistance to ensure successful implementation.
Guarantee your AI investment is cost-effective
The high cost of AI adoption remains a significant barrier, particularly for small farms. To make AI more financially viable:
- Conduct a cost-benefit analysis before investing in AI tools, evaluating potential savings in water, fertiliser, and labour.
- Determine the payback period – calculate how long it will take for AI to generate a return on investment.
- Utilise government incentives such as the Sustainable Farming Incentive, Farming Investment Fund, or R&D tax credits to offset AI-related expenses.
For smaller farms, shared AI investments can be a practical solution. Collaborative purchasing of AI tools among multiple farms can significantly reduce costs.
Start small and scale gradually
Instead of overhauling their entire operation, farmers should begin with a targeted AI application – such as disease detection or precision irrigation – before expanding usage based on proven success.
Align AI with farming goals
Not all farms prioritise high-yield efficiency. Those practising regenerative or ecological farming should select AI solutions that support sustainability efforts, such as soil health monitoring and biodiversity tracking.
Address data security and reliability
AI depends on vast amounts of data, raising concerns about privacy and ownership. Farmers must understand how their data is stored, who controls it, and how it is used. Additionally, AI should serve as a tool to enhance decision-making rather than replace human expertise.
Predictions for how AI will affect agriculture in the future
While some fear AI and automation will replace jobs, the reality is that AI will transform, rather than eliminate, farm work. Automation will fill labour gaps, and new roles will emerge in AI maintenance, data analysis, and precision farming.
Farmers will still be key decision-makers, using AI to enhance efficiency in tasks like weather forecasting and crop disease detection. While AI optimises farming processes, it cannot replace the deep expertise and experience that farmers bring.
Ultimately, AI is set to make agriculture more productive and sustainable – not obsolete. The future of farming will involve collaboration between farmers and AI to tackle agricultural challenges and meet growing food demands.
About the author
Ryan Sian, Managing Director of RandD UK, has over a decade of experience in R&D tax consultancy and holds a Chartership qualification. He has developed specialist knowledge in how to help companies to access funding opportunities for adopting new technologies.
