Revolutionizing Agriculture Through Artificial Intelligence
Artificial Intelligence has shaken the market since its inception, and this week’s news of China’s DeepSeek model triggering shockwaves on Wall Street shows this revolution is not slowing down.
For agriculture, AI & GenAI offer innovative solutions to enhance sustainability, boost productivity, and address farmers’ challenges. From AI-powered tools improving crop yields and advisory accuracy to generative AI accelerating R&D cycles, industry leaders are leveraging AI to transform agriculture at scale.
Corteva Agriscience, Syngenta, Bayer Crop Science, Benson Hill, Deloitte, Agrivi, and Digital Green weigh in on the effect of these innovations throughout the agri-food value chain, the evolution of AI applications, and the challenges that lie ahead for the sector.
January 29, 2025.
Impact: Driving Innovation in Agri-Food Value Chains with AI
Variables including unpredictable weather, pest and disease, soil health, input costs, and commodity prices all impact farmers’ decision-making. But “the sheer volume of data, different scenarios and scientific implications are very difficult for anyone to process, and this is where AI is helping farmers,” says Feroz Sheikh, Chief Information and Digital Officer at Syngenta Group.
Rikin Gandhi, CEO of Digital Green, uses Farmer.Chat, “an AI coach for smallholder farmers”, as an example of this. “By integrating dynamic data such as weather, soil, and market data, it increases [the] adoption of sustainable practices, boosts incomes, and reduces advisory costs. Challenges like data quality and digital literacy remain, but tailored AI product design and on-the-ground capacity-building overcome these barriers.”
He elaborates that “in Kenya, Farmer.Chat is improving agricultural call center operations, halving response times and increasing advisory accuracy”, while “AI-driven fertilizer and water management advisories have boosted crop yields while reducing input costs” in India. Syngenta’s Feroz Sheikh further spotlights AI’s advisory capabilities in relation to seed selection. “Our AI/ML models analyze soil data, weather predictions, and historical yields, recommending optimal seed varieties. This has resulted in up to a 25% yield increase in key markets.”
Beyond advisory models, AI supports product development in line with climate and market challenges. Brian Lutz, Vice President of Agricultural Solutions at Corteva Agriscience, says “AI has revolutionized agri-food value chains” by enabling tailored solutions. “AI-driven insights enable us to produce industry-leading seed and crop protection products, helping farmers combat the effects of unpredictable weather and pest infestations,” he explains.
Jason Bull, CTO at Benson Hill, shares this sentiment. He sees AI as being “pivotal in Benson Hill’s success, particularly in bringing new soy quality traits to the market”. Its CropOS platform, “fueled by over 470 billion data points, generates soybean breeding predictions with up to 85% accuracy.”
Sachi Desai, Vice President of AI Go-To-Market and Partnerships at Bayer Crop Science, further highlights AI’s ability to accelerate processes. “It’s significantly shortened crop breeding cycles and enabled us to develop crop protection products that exceed safety and sustainability criteria. With nearly one-third of the world’s arable land under agriculture, AI has the potential to revolutionize food production at scale.” This is echoed by Feroz Sheikh, who notes Syngenta’s “R&D pipeline is fully AI-enabled, helping us bring new innovations to the market faster.
Daniel Ferrante, Partner at Deloitte, uses the example of Atlas AI, a tool that “leverages generative AI to expedite R&D processes, reducing the 13-year average time-to-market and $136 million cost of GM crop development by identifying optimal targets early.”
Evolution: AI in Agriculture Over the Coming Years
“AI tools will become more sophisticated,” says Corteva’s Brian Lutz. “[We] continually integrate advanced robotics, IoT, and machine learning throughout our R&D efforts to accelerate the pace of new product innovation.”
Matija Zulj, Founder & CEO of AGRIVI, sees GenAI as a key enabler of digital transformation. “Generative AI has brought a major simplification of interactions between farmers and technology and will play a pivotal role in overcoming adoption issues of digital platforms, especially across medium and small-scale farming operations, which represent most of the global agriculture.”
This feeling is shared by Feroz Sheikh, as already “between the leading agtech platforms – Ops Center by John Deere, FieldView by Bayer and Cropwise by Syngenta, we have almost 1 billion acres digitally connected. This will continue to grow and become the norm.”
Deloitte’s Daniel Ferrante considers the role of automation and predictive modelling: “AI will power automated lab experiments, significantly reducing manual interventions and enhancing efficiency in R&D”. This sentiment is echoed by Benson Hill’s Jason Bull who adds, “This evolution means shorter development cycles, reduced input costs, and tailored solutions for feed, food, and fuel end markets.”
“These machine learning capabilities will continue to improve over the next years, and so will newer iterations like Generative AI (GenAI), which we’re fully committed to advancing for farmers and industry.” Says Sachi Desai.
GenAI Opportunities
“I am convinced that GenAI will play a key role in transforming agriculture,” asserts Sachi Desai. He details how Bayer’s recently commercialized expert GenAI model for agronomy called E.L.Y. is already saving more than 1,500 Bayer employees up to four hours per week when replying to enquiries, thanks to “its ability to upskill professionals, develop custom solutions on demand and ultimately better serve farmers.”
Similarly, at Corteva Agrisciences, GenAI is seen as a tool with potential “to revolutionize agriculture”. Brian Lutz shares how “Corteva has been a leader in aiding farmers through industry-leading agronomic research and support”, with the company this year “launching CARL (Corteva’s Agronomic Research Library), a new GenAI tool to enhance our customer support”.
Syngenta’s Feroz Sheikh adds that he believes “GenAI will have a profound impact on agriculture by lowering the barrier for adoption of AI tools”. He highlights its accessibility for smallholders: “GenAI can give them answers and recommendations in their native language that are easy to understand and implement.”.
Looking at pre-farmgate innovation, Brian Lutz says that “new GenAI models are accelerating the discovery of novel proteins and biologicals, promising advanced seed traits and crop protection products with superior performance and safety profiles.”. Jason Bull shares this sentiment. He highlights GenAI’s ability to simulate millions of scenarios, enabling breakthroughs in crop genetics. “At Benson Hill we are aggressively building a proprietary, world-leading data lake linking genetics to quality traits in soybeans,.” which includes “tailoring crops for specific dietary requirements in feed and optimizing carbon intensity.”.
Deloitte echoes this, seeing opportunities for nutritional enrichment, like “developing high-protein or micronutrient-enriched crops”, and increased resilience by “identifying genetic solutions for pest and pathogen resistance.
Challenges: Addressing Barriers to Adoption
“Despite its potential, AI in agriculture faces challenges such as data privacy, high implementation costs, and the need for robust infrastructure,” says Brian Lutz. He calls for “collaborative efforts, policy frameworks, and continuous innovation” – alongside “ethical and responsible AI deployment” – to meet these challenges.
Meanwhile, Feroz Sheikh warns that “the most significant challenge is the availability of quality data”.. “Poor quality data will lead to poor quality output, no matter how sophisticated the AI is.” Matija Zulj agrees “AI models are as good as the data they are trained on”, and so does Daniel Ferrante who notes the challenge of data integration when “handling and synthesizing diverse datasets for AI training”. Feroz Sheikh also stresses the need for democratized access to AI, particularly in developing regions and smallholder markets where “increasing sophistication of AI tools and their reliance on connected sensors and machinery can create a barrier to adoption.” This links to Matija Zulj’s call for responsible AI principles. He sees “Incorporating reliability, privacy, security, fairness, inclusiveness, transparency and accountability,” as critical, and he further stresses the importance of earning the trust of stakeholders by “focusing on AI use cases that work today to prove value delivery.”
And Jason Bull advocates for a “human in the loop” approach to AI, to utilize world-leading scientists with their decades of experience, rather than replacing them. He says, “This approach brings the best of both real-world experience and in silico prediction to bear for our innovation pipeline.” This strategy “mitigates model bias or incompleteness”, ensuring human oversight, and “ultimately enables robust data governance and clear frameworks for ethical AI use.”
This is Only the Beginning
Hear more leadership insights and join conversations at the World Agri-Tech Innovation Summit in San Francisco about how AI and GenAI are being used to redefine the future of agriculture.
Speakers addressing AI at the main plenary sessions on March 11-12 include Bunge, Syngenta Group, Microsoft and AWS. See summit program >>,
The pre-summit AI in Agriculture Forum on March 10 will host experts from Nvidia, Corteva Agriscience, Pairwise, Benson Hill, Ohalo, Driscoll’s, Digital Green, Microsoft, John Deere, Bayer Crop Science, FBN and more. See the full workshop program >>.
The post Revolutionizing Agriculture Through Artificial Intelligence appeared first on World AgriTech USA.
