Eleni is an experienced professional in the Ag seeds and biotech industry, with a focus on vegetable seed innovation using genomic technologies. As Head of Applied Data Science at Syngenta, she leads global teams to drive data-driven, sustainable solutions. Eleni holds a PhD in plant breeding and genetics.

In a recent interaction with Global Woman Leader Magazine, Eleni discusses how data science is reshaping agriculture, highlighting emerging trends and technologies, sustainability alignment, adoption challenges, and the vital role of leadership in fostering innovation, experimentation, and agility within the industry.

How is data science reshaping the Ag industry? Are there any emerging trends that will create significant disruptions over the next five years?

Data science and technology has already started to transform various aspects of Ag and allowed us to reimagine how to develop agricultural products (from seeds, to chemicals, to digital solutions) and how to grow crops (precision applications, soil and plant health monitoring, automated harvest).

When it comes to emerging trends, there is no doubt that the impact of artificial intelligence (AI) will continue to increase and provide us with unparallel power to predict future outcomes, make more targeted recommendations, improve decision making across the agribusiness value chain and solve pressing challenges of our industry. Besides AI, continuous developments in remote sensing, plant and environment sensors powered by Internet of Things (IoT) and robotics will allow Ag to further leapfrog.

What role will data-driven solutions play in addressing global issues such as climate change and food security? How can agriculture companies ensure that their innovations align with sustainability goals?

Data science is the cornerstone of accelerating speed and precision of crop improvement; therefore, it is critical part of the solution when it comes to food security since it allows us to design climate-resilient crops. These crops will be able to withstand different climatic conditions, grow in marginal soils, be more tolerant to drought, secure yields to meet the increasing demand for food, feed and fuel, and rely on lower inputs. This new wave of innovations will align with sustainability priorities, which have brought new sense of purpose across our industry, to secure higher yields with lower environmental impact, ensure more sustainable agricultural operations and practices, and increase adoption of regenerative Ag practices.

What strategies would you recommend for translating groundbreaking research into market-leading products while ensuring scalability and cost-efficiency?

Agricultural research, either in the private sector or academia and government, is grounded on the development of innovative products that will serve the value chain (from the grower up to the end consumer). This customer-driven mindset is what keeps us focused on the commercialization of market-leading products. The customer-first approach also means serving different types of customers with diverse agricultural operations (from highly sophisticated growers to small-holders with limited resources). Therefore, private and public investment and partnership are essential for funding development of diverse solutions to meet the needs of diverse growers across the globe.

How can data science teams effectively manage the nuances of various production systems? How can AI and Machine Learning further enhance precision agriculture in the coming years?

The data science tool kit we currently use to understand the impact of environment and crop management on each variety is better than ever before. Due to the convergence of various digital and genomic technologies, we can incorporate diverse data layers and create advanced analytical models to disentangle complex interactions and recommend how to achieve optimal crop performance in any production environment. As a consequence, machine learning (ML) and AI will continue to play a key role in making such models more accurate, enhance precision of data-driven decisions, and enable us to further improve crop performance in the most sustainable manner.

What will be the biggest barriers to widespread adoption of automation and digitalization at the farm level? How can companies work with stakeholders to overcome these challenges?

Reflecting on the lessons learned in R&D, the path to automation and digitization is typically not limited by resources or existing capabilities rather than by scalability of the given technical solution and simplification of data flows to enable end-to-end automation. I anticipate that these challenges will also become barriers in automation and digitization on the farm, depending on the size and complexity of operations. Additionally, the availability of broadband internet in remote agricultural areas is a critical enabler for technology adoption. Ultimately, investment in automation and digitization of agricultural practice will persist, no matter the challenge, to increase efficiency on the farm and reduce dependency on labor.

What role does leadership play in driving technological innovation in agriculture? How can leaders ensure that they are fostering a culture of experimentation and agility?

Leadership can empower meaningful change to further reinforce the path towards innovation powered by data and technology and driven by sustainability. With today’s pace of technological innovation, there has not been a better time for leaders in agriculture to shape the future! That said, leaders need to navigate an increasingly complex and dynamic ag business landscape while helping others to increase agility and build resilience. Fostering continuous learning and celebrating failures are essential for leaders to propel innovation in their teams. In addition, broadening partnerships can provide significant upsides as novel ideas, are born in diverse teams working towards common goals. Last, leaders can maximize impact by establishing collaborations with growers on the farm to experiment at scale and drive direct value.