Artificial intelligence is often associated with language models, robotics, or financial forecasting. Yet one of its most consequential applications lies in biology.
Biological systems involve dynamic genetic and environmental interactions that challenge conventional modelling. This makes them fundamentally different from the structured, largely static environments in which many early AI systems were first developed. Nowhere is this complexity more visible than in agriculture.
According to the United Nations, global food demand is projected to rise by more than 50 percent by 2050 as the world’s population approaches 9.7 billion. At the same time, the conditions under which crops are grown are becoming less predictable. Across the European Union alone, extreme weather events now account for an average of around €28 billion in agricultural losses each year – roughly 6 percent of annual crop and livestock production – with further increases expected under continued warming.
This widening gap between rising demand and growing environmental volatility presents one of the defining challenges for modern agriculture. Crops must produce more food, often on the same land base, while facing increasing exposure to heatwaves, drought, floods, and shifting seasonal patterns. Improving productivity is no longer simply about maximising yield; it is about maintaining stability under stress.
Why Complex Traits Are Difficult to Improve
For decades, traditional plant breeding has relied on crossing plant varieties and selecting the best offspring over multiple generations. This approach has been highly successful and remains essential today. However, it works most efficiently when traits are relatively simple and global climate conditions, both present and future, are putting this model under strain.
Many important characteristics, such as yield stability or tolerance to heat, are influenced by multiple genes across different gene regulatory networks. Each gene may have a small effect, but collectively they shape the plant’s performance. In some cases, a single gene can influence multiple traits at once, meaning that improving one characteristic may unintentionally affect another. This creates inherent trade-offs that make targeted improvement more complex.
When traits are controlled by networks rather than single genes, progress through trial and error becomes slower. Improvements can take many breeding cycles, especially when environmental conditions are more variable and extreme.
Moving From Trial and Error to Prediction
Artificial intelligence changes not only the tools available to researchers, but also the way decisions are made in crop development. Rather than relying solely on visible outcomes after a growing season, AI allows scientists to explore probabilities before planting even begins.
By analysing patterns across historical performance data, genetic information, and environmental records, machine learning systems can identify genetic configurations that are more likely to succeed under defined conditions. This shifts crop development from observation alone toward informed anticipation.
The difference is subtle but important. Instead of asking, “Which plants survived?” researchers can ask, “Which genetic patterns are statistically associated with resilience?” That shift in perspective influences how experiments are designed and where effort is concentrated.
Integrating Data Across Biology and Environment
This more predictive approach depends on the depth and quality of available data. Modern crop research generates detailed information not only about genetic sequences, but also about plant growth, stress responses, and performance across locations and seasons.
Environmental conditions add another layer. Temperature, rainfall, soil variability, and seasonal timing all shape how plants develop. When these environmental signals are analysed alongside genetic information, a clearer picture of crop behaviour begins to emerge.
Machine learning tools can help connect these layers, revealing how plant performance shifts under different scenarios. A variety that appears stable under moderate heat, for example, may respond differently under prolonged extremes. Understanding these patterns requires examining biology and environment together rather than in isolation.
From Understanding to Application
Insights from data analysis must ultimately be tested in real plants. Advances in genome editing now allow researchers to modify multiple genes simultaneously. This makes it possible to explore how combinations of genetic changes affect complex traits.
However, editing alone does not guarantee improvement. Plants must be evaluated carefully under controlled conditions and in the field. Field trials remain essential because they reveal how crops behave in realistic farming environments. This principle informs our development approach, which integrates computational modelling, targeted genetic changes, and systematic phenotyping. Data from field performance can then be fed back into models, improving their accuracy over time and turning research into an ongoing learning process.
A Broader Shift in Crop Science
Global food demand continues to rise, even as the conditions under which crops are grown become less predictable. Developments in AI are reshaping how crop development can respond to this uncertainty. As environmental patterns grow more complex, the ability to analyse large, interconnected datasets and generate forward-looking insights becomes increasingly important. The future of crop improvement will depend not only on biological discovery, but on how effectively data-driven approaches help anticipate and adapt to change.