How Artificial Intelligence (AI) is transforming crop and seed innovation
Artificial intelligence in agriculture is beginning to reshape how new crops and treatments are invented. The implications go well beyond technology.
Why AI in agriculture matters now
For most of the past century, agricultural innovation has followed a predictable rhythm: observe, test, learn, repeat. Breeders coax new plant varieties into being; scientists concoct promising treatments; both are then subjected to years of trials in laboratories and muddy fields.
The method has fed a growing world, more or less. But as the low-hanging fruit has been picked, new gains are proving harder to reach, and the rate at which genuinely new crop-protection products reach the market has been falling for decades.
Artificial intelligence arrives at an opportune moment. Our new white paper, How Artificial Intelligence is transforming agricultural innovation, draws on voices from across the sector to show how AI is being applied across the agriculture innovation lifecycle – and its potential to deliver faster, more targeted, and more sustainable product development.
From dry labs to generative design
One of the most talked-about changes is the "dry lab", in which more experimentation happens in silico, with AI applied to a company's own data to create designs of new ingredients or formulations, so that physical trials begin further along and run more efficiently.
The promise is not new, but newer forms of generative AI are unlocking fresh value. Whereas ‘traditional’ AI learns patterns from historical data to make predictions such as whether a seed treatment will improve germination, a generative model can go a step further by proposing an entirely new formulation for scientists to evaluate.
How AI can accelerate crop and seed innovation
AI for genetics and trait prediction
Genetics is proving unexpectedly amenable, for a pleasingly simple reason. “DNA is basically a sequence of letters, which means it is easier to process with today's language models," Tonny Otjens of Rijk Zwaan told us.
His firm is exploring how such models can combine phenotypic observations with genetic data to predict which varieties will deliver desired traits – insights that could also drive the fast-growing market for biological coatings and treatments.
AI is also opening up possibilities beyond genetics. Incotec has developed an X-ray imaging tool for tomato seed embryos where AI compares images with historical germination data and identifies markers that predict the likelihood of successful germination.
Dr Marta Dobrowolska-Haywood, Head of Data Science and Knowledge Management, Croda Agriculture
Automating documentation and compliance
Not every gain is so cerebral. Some of the most popular applications simply strip away drudgery, automating the documentation and compliance work that devours researchers' time. Rijk Zwaan built a tool for the thankless task of naming products, screening candidates against trademark, linguistic, and regulatory requirements.
What once took weeks – and could end in rejection if a name echoed a Danish rival or a Spanish swear word – now takes minutes. Unglamorous, but obviously useful. And such quick wins often convince sceptics that the technology is worth the bother.
Data and scientific expertise create lasting advantage
A central insight from the whitepaper is that AI alone does not create a lasting advantage. The frontier models everyone marvels at are, in the words of BASF's Stephan Köhler, ‘commodities’. Advantage comes from combining them with the best proprietary data, scientific expertise and market insight.
For Dr Stephan Köhler of BASF, this combination is where the opportunity for competitive advantage lies.
Dr Stephan Köhler, Principal Scientist for the modelling of active ingredient formulations, BASF
Organisations with significant proprietary data, whether created or acquired, will be better placed to build useful models. For example, the years Rijk Zwaan spent photographing plants for observational studies have become a treasure trove of research and development data.
Rethinking the agricultural R&D process
But AI also heralds change in how the work is done. Formulation scientists, Dr Köhler notes, typically use experience to pick promising starting points, then refine them through experimentation.
In an AI world, the sequence partially inverts: teams generate large volumes of data first, train models on the results, then use those models to explore possible solutions. That shifts the duller, repetitive work to the very start of the process but expands the options and accelerates optimisation later.
The biggest AI challenge is organisational
The white paper argues that the main obstacle is organisational rather than technological. Agriculture will need better data, gathered and standardised with future models in mind. It also needs new organisational thinking and structures; new ways of working; a rigorous, auditable approach to trust and explainability; and an AI-ready workforce comfortable directing models rather than merely using them.
Building the right AI ecosystem
Progress will also depend on an ecosystem of technology and research partners beyond anything traditional agricultural R&D is used to.
Explore the future of AI in agriculture
None of this will be easy. This article captures only part of the opportunity. The white paper also explores knowledge graphs, foundation models, AI audits, human-AI teams, and the first steps leaders can take.
For anyone in the agriculture business of growing things – and wondering how the biggest shake-up of agriculture in a generation will affect them – it is a good place to start.
Discover how artificial intelligence is influencing crop and seed innovation, from R&D and generative design to data, skills, and new ways of working. Download the white paper today: How Artificial Intelligence is transforming agricultural innovation
Whitepaper: Artificial Intelligence
