In recent years, the fusion of technology and agriculture—known as AgTech—has revolutionized an industry that was once rooted in traditional methods. Among the most transformative innovations is Artificial Intelligence (AI), a powerful tool poised to enhance productivity and address global challenges.
AI is at the heart of agriculture’s digital transformation, helping farmers navigate an increasingly complex landscape. Climate change, dwindling natural resources, and the growing demand for sustainable practices require rapid, data-driven decision-making. By leveraging AI-powered insights from sensors, drones, satellites, and other connected technologies, farmers can predict climate patterns, monitor crops in real-time, and automate processes—leading to greater efficiency and less waste. AI doesn’t just reshape how crops are managed; it redefines the relationship between producers, consumers, and the entire supply chain.
This article explores the impact of Artificial Intelligence on agriculture, examining key technological trends, real-world benefits, and the challenges that remain. Through insights from industry leaders, interviews, and testimonials from key figures in Brazilian agribusiness, we present a detailed analysis structured around three core pillars:
- Data for Predictive Agriculture
- Farmer Experience Over Transactions
- Agricultural Companies Becoming Tech Companies
Agribusiness is entering a new era with Agriculture 5.0, the next evolution of Agriculture 4.0. This transformation integrates advanced technologies like Artificial Intelligence (AI) into an interconnected ecosystem, moving beyond automation and connectivity to enable machine learning and autonomous decision-making. With these capabilities, agricultural systems can not only collect data but also interpret information and take intelligent actions—driving significant gains in productivity and sustainability.
A fascinating example of AI’s transformative potential in agriculture emerged indirectly during the 2024 Nobel Prize Ceremony. This historic event showcased not only human ingenuity but also artificial intelligence being recognized at the highest level. For the first time, an AI-driven breakthrough earned the Nobel Prize in Physics, awarded to a team from Google’s DeepMind, led by Demis Hassabis and John Jumper. Their creation, AlphaFold, revolutionized science by accurately predicting protein structures—a challenge that had stumped researchers for decades.
Just months after receiving the prestigious award, Google unveiled AlphaFold 3, an even more advanced version capable of predicting the behavior of human molecules. This breakthrough raises profound questions: Should recognition go to human intelligence for designing AI, or to AI itself for achieving what was once thought impossible? And how far will AI go in reshaping industries and society?
AlphaFold’s ability to generate new molecules has enormous implications—not just for pharmaceuticals, but also for agribusiness. AI is increasingly being used to tackle some of the world’s most pressing challenges, including food security and sustainable farming. A prime example of this is Iktos, a French startup specializing in AI-driven molecular generation, which recently partnered with Bayer to develop sustainable crop protection solutions. Using Iktos’s de novo generative design software, Makya™, Bayer scientists can accelerate the discovery and optimization of novel molecules, helping to create more effective and sustainable agricultural solutions.
This is just one example of how AI is transforming the field. The adoption of AI in agriculture is rapidly increasing, with market projections reflecting its growing impact. In 2025, the AI in Agriculture market is expected to reach USD 2.4 billion, and by 2030, it is projected to nearly triple to USD 6.4 billion.
Leading the way in this AI-driven transformation is John Deere, which has evolved from a traditional agricultural equipment manufacturer into a technology-driven company that empowers farmers with intelligence and data-driven solutions. This shift reflects the company’s strategic adaptation to the digital age, recognizing the immense value of data and software in modern agriculture.
John Deere’s AI strategy is centered around three key areas:
- John Deere Operations Center – The company plans to connect 1.5 million machines and 500 million acres of farmland to its cloud-based platform by 2026. This center collects and stores vast amounts of crop data, including millions of weed images for targeted herbicide application.
- Autonomous Machinery – John Deere has developed fully autonomous tractors and is investing in technology to retrofit older equipment with autonomous capabilities, making advanced AI accessible to more farmers.
- Precision Agriculture – Leveraging satellite imagery and AI algorithms, John Deere provides farmers with actionable insights for irrigation, fertilization, and crop protection, optimizing resource use and boosting yields.
With this AI-powered transformation, John Deere has set an ambitious goal: by 2030, 10% of its annual revenue will come from software fees—a bold move that underscores how AI is reshaping the future of agriculture.
It is now clear that AI is redefining success in agribusiness. With that in mind, let’s take a closer look at the three key pillars driving AI’s transformation of agriculture.
- Data for Predictive Agriculture
Imagine this: when agricultural equipment is operating in the field—perhaps in the middle of the U.S.—do you assume it’s online or offline? For decades, agribusiness has been one of the least digitally connected industries due to its vast, decentralized nature. However, that is rapidly changing. As of 2023, 21.3% of U.S. agricultural land still lacks internet access, creating a barrier to adopting advanced technologies. While farm connectivity has improved—78.7% of U.S. farm operations were online by 2022—speed and reliability remain major concerns.
For instance, the Federal Communications Commission (FCC) defines broadband as 25 Mbps download and 3 Mbps upload, but experts suggest that next-generation technologies, such as autonomous machinery, will require speeds closer to 300 Mbps. Without high-speed, reliable connectivity, the adoption of precision agriculture techniques is significantly hindered, potentially impacting crop yields and operational efficiency.
This is where partnerships like John Deere and SpaceX’s Starlink are making a difference. Together, they are tackling rural connectivity challenges through satellite communication (SATCOM) solutions tailored for agriculture. John Deere’s SATCOM service integrates ruggedized Starlink terminals and cellular modems, enabling real-time data sharing, remote diagnostics, and machine-to-machine communication.
Currently, this initiative is in an early access phase in the U.S. and Brazil, aiming to connect 1.5 million machines. A limited release is planned for late 2024, with broader adoption expected by year-end—marking a significant step toward a more connected, AI-driven future for agriculture.
Once connectivity is established, the next challenge is managing data—both its sheer volume and its practical utility. The number of IoT-enabled devices in precision farming and agricultural equipment is projected to reach nearly 300 million by the end of 2024, with further growth to 379 million by 2026. These IoT applications are transforming agribusiness by enabling precision irrigation, soil monitoring, disease tracking, and fertilizer management. However, this surge in data presents a new challenge: how can farmers effectively process and act on such vast amounts of information?
This is where AI becomes a game-changer. AI algorithms power the development of “smart farms”—interconnected ecosystems that collect, analyze, and act on data in real time. These farms shift agribusiness from reactive decision-making to predictive strategies, allowing for proactive planning and risk mitigation.
For instance, AI can:
- Forecast weather patterns with greater accuracy.
- Detect crop diseases early using advanced image recognition.
- Recommend optimal planting and harvesting times based on environmental data.
These AI-driven capabilities parallel advancements seen in healthcare diagnostics, where machine learning enhances forecasting, early detection, and decision-making.
Key Principles of Smart Farming:
- Data-driven decisions
- Unified cloud-based platforms
- Systems automation
- IoT integration for precision agriculture
- Predictive analytics
- Remote monitoring and edge computing
- Future technologies like digital twins (simulations of physical farms)
By combining AI with IoT, cloud computing, and automation, smart farming is not just improving efficiency—it is redefining the future of agriculture.
A standout example of AI’s transformative impact on agribusiness is Bayer’s Climate FieldView. This platform integrates data from equipment sensors, satellites, and other sources to provide farmers with actionable insights for planting, spraying, spreading, and harvesting. By leveraging machine learning, Climate FieldView analyzes yield data at a highly granular level, enabling farmers to refine their agricultural practices for greater efficiency and sustainability.
Recent advancements have further enhanced the platform’s capabilities. Bayer introduced a generative AI tool trained on proprietary agronomic data and expert insights. This tool helps farmers by:
- Optimizing input usage to reduce waste and increase efficiency.
- Detecting crop diseases early for proactive intervention.
- Providing adaptive, data-driven solutions to environmental challenges.
The adoption of such advanced AI tools represents a fundamental shift in agriculture. Data and AI are no longer just enhancements—they are core pillars driving the transformation of agribusiness. By eliminating inefficiencies and replacing guesswork with precision and predictive analytics, these technologies are setting the stage for a more sustainable and productive future.
With connectivity expanding and AI platforms evolving, the industry is steadily advancing toward fully autonomous farming systems—a vision that John Deere aims to achieve by 2030. The future of agriculture is no longer just about growing crops; it is about growing intelligence.
2 – Farmer Experience Over Transactions
In today’s hyper-connected world, farmers—just like consumers—are demanding better digital experiences from agribusinesses. The reason is simple: they are already accustomed to personalized, seamless interactions in other aspects of their lives, whether through ride-sharing services like Uber, digital banking, or e-commerce platforms. These experiences have set a new standard, and farmers now expect the same level of convenience, efficiency, and personalization from their business partners.
If agribusiness companies still believe that offering the best products and services is enough, they are mistaken. Success today is not just about what you sell—it’s about the experience you provide. The difference between selling a product or service and delivering a great experience lies in how you make customers feel throughout the entire journey.
A recent McKinsey survey highlights key challenges farmers face when interacting with digital platforms in agribusiness:
- 50% of farmers identify customization and experience as their biggest challenges when buying online.
- 57% feel that the platforms they use fail to provide adequate customer service.
- 29% don’t trust the self-service purchasing process.
These insights reveal a critical gap in how agribusinesses are addressing evolving customer expectations. Farmers’ experiences are increasingly shaped by the best digital interactions they encounter outside the agricultural sector. This shift underscores the need for hyper-personalization, frictionless processes, omni-channel accessibility, and instant responsiveness—factors that will define the future of digital engagement in agribusiness.
A strong example of this transformation is Bayer’s acquisition of Orbia, a Brazilian online marketplace designed for farmers. Unlike traditional models focused solely on selling Bayer products, Orbia offers a diverse range of brands, including those of competitors. This move reflects a fundamental shift: agribusinesses must embrace one-stop-shop platforms that allow farmers to manage multiple aspects of their operations in a single, seamless digital environment.
Platforms like Orbia are more than just digital storefronts. They represent a strategic shift toward delivering value through convenience and choice, even if it means generating revenue from competitors’ products. This omni-channel approach aligns with the future of agribusiness, where digital platforms play a central role in solving farmers’ day-to-day challenges, streamlining procurement, and optimizing farm management.
This shift is not limited to farmers. Consumers, too, have rising expectations for transparency and access to information. They increasingly want the ability to scan a QR code on their food packaging to trace its origin, verify sustainability practices, and ensure ethical production. As digital platforms continue to evolve, the demand for greater visibility and accountability in the agricultural supply chain will only grow.
AI-powered precision farming is reshaping sustainability in agriculture by driving measurable improvements:
- 21% increase in crop yields
- 26% reduction in fertilizer use
- 34% decrease in pesticide usage
These figures highlight how AI can enhance both sustainability and productivity. However, this transformation also creates challenges for agribusinesses that have traditionally relied on selling fertilizers and pesticides. As AI-driven efficiencies reduce the need for these inputs, companies face what Clayton Christensen famously termed the innovator’s dilemma—if they fail to adapt and address their customers’ evolving needs, others will step in.
A strong example of overcoming the innovator’s dilemma comes from the pharmaceutical industry. Eli Lilly, for instance, shifted its focus from insulin production to weight-loss drugs like Monjaro. While this transition reduced the company’s reliance on its traditional insulin business, it positioned Eli Lilly as a global leader by addressing a growing market demand: combating obesity.
For agribusinesses, the lesson is clear—long-term success depends on adapting to technological advancements and shifting market needs rather than resisting change.
Agribusinesses must embrace this same mindset, continuously reinventing themselves to address emerging customer challenges, even when it disrupts their established business models. This adaptability is essential for maintaining relevance in an ever-evolving market.
Generative AI is another transformative force in improving both farmer and consumer experiences. However, large language models like ChatGPT are not yet precise enough for agriculture-specific applications. A study published in Nature Food highlighted inaccuracies in professional advice provided by these models to farmers in Africa, underscoring the potential risks of relying on generalized AI solutions for highly specialized fields.
The solution lies in small language models—AI systems trained specifically for industry needs. Bayer has already piloted such a tool, built on proprietary data and agronomists’ expertise. This generative AI solution provides actionable insights and personalized recommendations, directly addressing the unique needs of farmers and advisors.
According to McKinsey, the incremental value of generative AI for companies across the agricultural value chain is significant, with potential revenue impacts exceeding 2.5% across operations, marketing, and sales. By prioritizing small language models, agribusinesses can unlock AI’s full potential to deliver precise, high-value experiences tailored to the industry’s demands.
To succeed in the future, agribusinesses must move beyond simply selling products and focus on delivering transformative experiences. Whether through hyper-personalization, sustainability, or advanced AI solutions, the objective remains the same: meet the evolving needs of farmers and consumers by providing seamless, transparent, and innovative solutions that enhance efficiency and trust.
- Agricultural Companies Becoming Tech Companies
The third and final pillar to explore is the transformation of agribusiness from traditional farming enterprises into technology-driven organizations—a shift from agribusiness toward big tech. No longer solely focused on working the fields, these companies are rapidly becoming technology-powered enterprises, leveraging AI, automation, augmentation, and simulation to redefine their operations and impact.
A striking example of this transformation is NVIDIA’s Earth-2 project. Launched in 2021 and fully operational by 2024, Earth-2 is a cloud-based platform designed to simulate and predict global climate patterns using AI and high-performance computing. By combining ultra-high-resolution simulations with unprecedented processing speeds, Earth-2 is shaping the future of climate forecasting and agricultural planning.
Several key features demonstrate its potential impact on agribusiness:
- AI-powered simulations: Earth-2 uses advanced AI models such as CorrDiff and FourCastNet to generate climate and weather simulations with an extraordinary resolution of 2 kilometers. This level of precision is 12 times more detailed than traditional methods, enabling highly localized and accurate forecasting.
- Speed and efficiency: Leveraging cutting-edge GPU technology, Earth-2 delivers forecasts in seconds, a dramatic improvement over conventional CPU-driven models that take minutes or even hours. It is also 1,000 times faster and 2,000 times more energy-efficient than current numerical weather prediction systems.
- Global applications: Organizations worldwide are already integrating Earth-2 into their operations. The Weather Company incorporates it into its Weatherverse tools for enhanced visualization, while Taiwan’s Central Weather Administration applies it to improve typhoon predictions and evacuation planning.
As agribusiness continues to evolve into a tech-enabled industry, tools like Earth-2 illustrate the growing intersection between agriculture and AI. The ability to simulate and predict environmental conditions with such precision will be a crucial advantage in ensuring food security, optimizing resource management, and mitigating climate-related risks.
For agribusinesses, the implications of these advancements are profound. Tools like Earth-2 enable farmers and agricultural companies to better anticipate weather-related risks, optimize planting schedules, and enhance supply chain planning. This growing intersection between big tech and agriculture is reshaping how the industry approaches efficiency, sustainability, and resilience.
While some agribusinesses may develop technology solutions in-house, many will need to embrace open innovation. A concept introduced by Henry Chesbrough, open innovation recognizes that not all ideas must originate within a company—collaborating with external partners, startups, and research institutions is crucial for staying competitive in a rapidly evolving market.
The agrifood sector has already shown a strong commitment to this approach:
- In 2023, 86% of agrifood companies reported increased investment in open innovation, with 93% planning to continue over the next three years.
- More than 90% collaborated with universities and research centers, yet only 50% engaged in startup scouting, highlighting significant untapped potential in working with emerging innovators.
As technology continues to drive transformation, the ability to harness both internal expertise and external innovation will define the future success of agribusinesses. Those that proactively integrate new ideas, technologies, and partnerships will be best positioned to lead in the next era of agriculture.
This shift toward open innovation aligns with the growing need for agribusinesses to become tech-enabled. Startups are at the forefront of developing solutions for precision agriculture, robotics, and AI-powered analytics. By scouting and collaborating with these innovators, agribusinesses can gain access to cutting-edge technologies that would otherwise take years to develop in-house.
The transformation from agribusiness to big tech comes with challenges. Companies must navigate obstacles such as integrating new technologies into legacy systems, training employees to adapt to tech-driven processes, and overcoming cultural resistance within traditionally conservative industries. However, the potential rewards—greater efficiency, improved sustainability, and the ability to anticipate and respond to challenges in real time—make the shift essential.
NVIDIA’s Earth-2 is a clear example of what becomes possible when agribusiness embraces technology. Likewise, open innovation creates opportunities for collaboration, bringing together the best minds in tech and agriculture to solve some of the industry’s most pressing problems. By adopting these strategies, agribusinesses can position themselves not just to survive but to thrive in a landscape where technology is the driving force behind success.
The future of agribusiness lies in thinking like a tech company—leveraging innovation, partnerships, and advanced digital tools to build a smarter, more sustainable agricultural ecosystem.
You can reach Andrea at his social media profiles:
LinkedIn: https://www.linkedin.com/in/andreaiorio/
Instagram: https://www.instagram.com/aiorio_br/
