The first farmers wrote their stories in seeds, selecting and saving the strongest, the sweetest, and the most abundant. Ten thousand years later, in laboratories across the globe, we're still writing agricultural stories – but now our ink is algorithms, and our paper is the double helix of DNA itself.
For millennia, this story of agriculture was written slowly, each generation adding just a few lines to humanity's growing manuscript of botanical knowledge. A sweeter tomato here, a hardier wheat there – progress measured in centuries and seasons. But today, artificial intelligence is rewriting the rules of authorship itself. What once took decades can now be drafted in days, as machine learning algorithms compose new possibilities in the language of life.
This is not merely a story of speed, however. In the face of a changing climate and growing global population, it's becoming clear that humanity's agricultural story needs not just new chapters, but an entirely new way of writing them. The challenge isn't simply to grow more food – it's to grow smarter food, more resilient food, in ways that won't exhaust the very soil from which our stories spring.
Enter the new authors of agricultural innovation – not replacing the farmer's ancient wisdom, but augmenting it with tools that can read and write in the alphabet of adenine, thymine, guanine, and cytosine. These molecular letters, when properly arranged, spell the difference between crops that thrive and those that merely survive in our increasingly unpredictable world.
The grammar of growth
In a single season, a modern crop field generates more data than all the agricultural texts written in human history. Every soil pH fluctuation, every minute variation in moisture content, every subtle shift in nutrient levels – all recorded, analyzed, and interpreted by artificial intelligence. But this torrential flow of field data is just one chapter in a much larger story.
Global agribiotech leaders sit atop vast repositories of crop and genetic data, accumulated through decades of research and field trials. This proprietary data – from genomic sequences to crop performance across countless growing conditions – forms the essential foundation for AI-driven agricultural innovation. While a handful of tech companies currently provide the computational infrastructure, it's these agricultural datasets that hold the key to unlocking AI's potential in crop science.
"For millennia, the success of crops and even entire societies has depended on man's ability to forecast weather and other growing conditions," reflects Feroz Sheikh, Chief Information and Digital Officer at Syngenta Group. What's changing now, he explains, is our ability to not just forecast, but to fundamentally reshape our agricultural future: "AI is helping us accelerate our R&D to create better, more resilient crops as well as next-generation solutions for plant health."
The new vocabulary of variation
The term 'reading' takes on new meaning in modern agriculture. Consider this: in the time it takes you to read this article, Illumina's technology will have analyzed the genetic markers of hundreds of crop samples. "More than 10 million DNA samples derived from crops and livestock have been genotyped using Illumina's Infinium technology in 2023," reveals Evgeny Glazov, "which is roughly 20 samples every minute."
But reading is just the beginning. The true revolution lies in understanding – and it's here that artificial intelligence becomes not just useful, but essential. Imagine trying to spot a single meaningful variation in text billions of letters long, written in a language where the smallest typo could mean the difference between drought resistance and vulnerability. Now imagine doing that millions of times over, searching not just for single-letter changes but for complex patterns that might span entire chapters of the genetic story.
This transformation of agricultural genomics is already reshaping farming practices across the globe. "Agricultural genomics is driving sustainable productivity and offers solutions to the mounting challenges of feeding a growing global population," explains Robert McBride, Illumina's General Manager Intercontinental. "Using modern technology, farmers, breeders, and researchers can easily identify the genetic markers linked to desirable traits, informing cultivation and breeding decisions."
This is where companies like Bayer are redefining what's possible. "We have built the most technically enabled precision breeding platform in the industry," explains Mike Graham, Head of Breeding at Bayer's Crop Science Division. "The unparalleled quantity and fidelity of the data we collect, combined with today's genomic capabilities and artificial intelligence, allows us to design the next generation of seed products tailored to specific geographies and built to meet customer needs."
The numbers are staggering. "Every part of our breeding and our global operations today is impacted by AI," Graham continues. "We globally deploy 400 to 500 new seed products every year and each product advanced goes through an average of 140 data science models to reach commercialization."
From page to field
Theory becomes a practice in unexpected ways. In 2022, Syngenta deployed what might be called agriculture's most ambitious proofreading system – the world's first commercial digital solution for diagnosing nematode infestations in soybean crops. "This digital tool uses a unique, proprietary algorithm to analyze images of fields obtained by satellites," explains Thomas Jung, Chief Data Officer at Syngenta Group. The system doesn't just spot trouble; it predicts potential losses and suggests preventive measures before the damage becomes visible to the human eye.
This is the new reality of agriculture – where satellite imagery, genetic markers, and artificial intelligence converge to write better endings to age-old farming challenges. The results are tangible: Syngenta's smart fertilizer mapping system alone can reduce fertilizer use by up to 20% while increasing yields by 15%, transforming efficiency from an environmental aspiration into an economic reality.
Yet this revolution extends beyond individual innovations. "We use AI and data science to develop better seeds and more desirable plant traits, and to bring new crop protection solutions to market faster," says Jeremy Williams, Head of Digital Farming for Bayer's Crop Science Division. "AI can also be the foundation for agronomic recommendations that support crop management decisions such as when to plant and irrigate, that is tailored to the farm so that more can be produced from the same piece of land and more people can be fed without starving the planet."
The scale of this transformation becomes clear in Bayer's breeding operations, where artificial intelligence has compressed time itself. "These new capabilities are enabling us to reduce the time required for a breeding cycle by up to 15X, cut overall product development timelines by 2 years, and ultimately increase the rate of genetic gain by 2X in 2030," says Graham. What once took generations can now happen within a single season.
The new co-authors
While biotech companies rewrite crop genetics and agricultural giants reinvent farming practices, traditional agricultural chemical companies like BASF are finding their own place in this unfolding narrative. The world's largest chemical producer is evolving beyond its historical role of developing crop protection products, embracing digital agriculture through its xarvio platform. Here, artificial intelligence doesn't just optimize chemical applications – it fundamentally rethinks the relationship between crops and their protectors, suggesting precisely when, where, and how much intervention is needed.
Thermo Fisher Scientific, meanwhile, bridges multiple chapters of this story. With a comprehensive suite of analytical tools and technologies, they're helping translate genetic discoveries into practical agricultural solutions. Their equipment and software enable researchers to move seamlessly between understanding crop genetics and improving field performance, creating a continuous narrative from laboratory to harvest.
Editing tomorrow
But perhaps the most profound change isn't in any single technology or technique – it's in how we think about agriculture itself. Humanity moving from a world where we simply select the best existing crops to one where we can envision and create the crops we need. As Illumina's Glazov notes, "Genomics and multi-omics data will continue to be foundational for AI and ML approaches that will transform our understanding of genetics and biology of domesticated crops."
"Having said this," Williams of Bayer cautions, "it is important to highlight that there is no one-size-fits-all. With every new season, farmers face unique environmental factors and pest pressures, all requiring careful consideration and management." This is perhaps the most crucial lesson in agriculture's new chapter – that for all our technological sophistication, we must still respect the complexity and diversity of the natural world.
The story continues
As this new chapter in agricultural history unfolds, the possibilities appear boundless. However, with each innovation comes added responsibilities. "At Syngenta, the belief in the transformative power of AI in revolutionizing agriculture is strong," Sheikh states, "by enhancing efficiency, driving productivity, and improving sustainability." Yet, he underscores the importance of this revolution serving a broader purpose – developing advanced tools that expand farmers' insights while bridging the gap between end-consumers and farm producers.
This connection – linking the laboratory to the field, code to crop, and producer to consumer – is arguably the most significant narrative yet to unfold. As articulated by Bayer's Williams, the objective is to ensure "more can be produced from the same piece of land and more people can be fed without depleting the planet."
In many respects, the journey has come full circle. The initial farmers who meticulously selected and preserved their finest seeds were, in their way, programming the future – embedding their aspirations for better yields into the genetic makeup of their crops. Contemporary agricultural innovators continue this essential mission, equipped with technologies those early farmers could scarcely have envisioned.
The difference now lies in scale and velocity. Processes that once spanned centuries can now be accomplished in seasons. Tasks that require generations of meticulous observation can presently be forecasted by algorithms before planting a single seed. As demonstrated by Illumina's technology, with the capability to process twenty crop DNA samples every minute, the agricultural community is not merely reading and writing the language of life – it is fluently speaking it, in real-time.
Despite the profound mastery over this new dialect, the fundamental elements of growth remain unchanged: soil and sun, water and air, and the perpetual cycle of seasons that has steered agriculture from its beginnings. What has transformed is our capacity to comprehend and adapt to these age-old rhythms, employing artificial intelligence to ensure that the narrative of agriculture remains one of abundance, resilience, and hope.
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