The Digital Transformation Of Agricultural Research Is Already Underway

By Ram Dhulipala, Director, CGIAR Digital Transformation Accelerator

28 May 2026, Nairobi: AI is often described as a revolution waiting to happen in agriculture. On the ground, the digital transformation of agricultural research is already underway, reshaping how we collect data, generate knowledge, and deliver solutions to those who feed the world.

At CGIAR, we see this transformation every day. Across our global network, our 9,000 researchers are using AI, machine learning, remote sensing, cloud computing and other advanced analytics not as futuristic concepts, but as a daily aid to address some of the planet’s most pressing challenges: climate change, food insecurity, water scarcity, and biodiversity loss.

The real question is no longer whether digital technologies will transform agriculture. It is whether public agricultural research systems can evolve fast enough to ensure these technologies benefit everyone, especially smallholder farmers in the Global South.

Agricultural research has always been a data-intensive enterprise. For decades, scientists have generated enormous volumes of information through field trials, lab experiments, genomic sequencing, surveys and climate impact monitoring. What has changed is our ability to connect, analyze and act on these data at unprecedented scale.

For us, this transformation is happening across four dimensions of agricultural research.

The first is data collection itself.

Agriculture is increasingly observable in real time. Sensors, satellite imagery, smartphones and drones allow researchers to capture information continuously across landscapes and farming systems.

A good example is the CGIAR’s work on digital phenotyping – the process of measuring the performance of crop crosses under real-world conditions to assess their potential to become the next improved variety. Every season, more than 150 CGIAR breeding stations worldwide generate massive amounts of field data as breeding teams evaluate crops for drought tolerance, disease resistance and productivity. Traditionally, phenotyping relied on millions of manual observations, making it slow, laborious, and difficult to standardize globally.

Today, CGIAR researchers are using drone-based image analysis, smartphone applications mounted on carts and AI-powered computer vision systems to automate much of this process. By aligning data standards across nine breeding Centers, we are building interoperable systems that allow crop data to be collected and analyzed consistently worldwide.

In partnership with Google Research, we are now taking this even further by developing what we call a “digital brain” for crop phenotyping: an AI-powered system capable of interpreting images and data from thousands of field trials across crops and environments. For breeders, this means faster insights, more accurate selection decisions and ultimately better crop varieties reaching smallholder farmers sooner.

The second transformation is analytical capability.

Today’s agricultural challenges are deeply interconnected. Crop performance depends not only on genetics, but also on climate, water availability, soils, pests, etc. Traditional analytical approaches often struggle to integrate these complex systems.

AI changes that.

Machine learning and advanced analytics now allow researchers to combine multimodal datasets — from genomics to weather patterns to socioeconomic indicators — into unified analytical frameworks. High-performance computing clusters make it possible to process these data at scales that were unimaginable only a few years ago.

Hydro.io, one of CGIAR’s newest initiatives, illustrates this shift. Developed to help governments answer a simple question: “Will there be enough water in the future?”, the platform creates an integrated decision-support ecosystem. 

In regions such as the Limpopo River Basin, where water decisions affect millions of people across multiple countries, fragmented data and disconnected models prevent effective planning.

Hydro.io is based on a robust data model, combining remote sensing, data analysis, field monitoring, open climate platforms, and local knowledge, while taking into account user needs and a human-centered design. These elements are integrated into interactive digital twins that allow policymakers, hydrologists, water managers, and researchers to simulate different scenarios, assess trade-offs, and anticipate future water trends.

Then, a suite of AI agents automates forecasting, monitoring, and scenario analysis. These agents power dashboards, respond to user questions, and provide live insights to all stakeholders.

By providing better technology, the ecosystem is fundamentally changing how evidence informs water governance for everyone’s benefit. 

The third transformation is knowledge management.

Agricultural science produces vast amounts of valuable knowledge, but much of it remains difficult to access, fragmented across repositories or buried in highly technical documents. AI-powered knowledge systems are helping change that.

At CGIAR, we are exploring how language models, intelligent search systems and AI-powered speech recognition tools can improve how scientific knowledge is organized and retrieved. Through a partnership with Farm Radio International, which manages interactive radio projects in 12 African countries, we developed Longa, a natural language processing model that automatically transcribes, translates, and analyzes thousands of farmers’ voice messages, transforming them into actionable data to improve the relevance and quality of agricultural advice.

Longa understands low-resource languages that had never been considered by AI before. For the first time, it gives a voice to populations that had never been heard by scientists.

This is particularly important in agriculture, where knowledge needs to be rapidly transferred from the labs to the fields.

Which leads to the fourth transformation: dissemination.

Digital technologies are therefore becoming the bridge between scientific discovery and the real world. AI-enhanced search and chatbot systems are making complex scientific information more accessible to policymakers, extension agents and farmers.

One example is AgriLLM, CGIAR’s specialized large language model for agriculture. Unlike general-purpose AI systems, AgriLLM is trained specifically on agricultural knowledge, using more than 146,000 curated question-and-answer pairs from CGIAR and over 12 global partners.  

The goal is simple and powerful: provide farmers and extension agents with reliable, context-specific guidance tailored to crops, regions and local realities. In many parts of the world, where extension services remain limited, such system will dramatically expand access to actionable agricultural knowledge.

CGIAR’s approach to AI is rooted in public goods, so AgriLLM is being developed as an open-source system intended to benefit the global agricultural community. 

Also Read: China’s Fertilizer Trade Sees Strong Export Growth in Jan–April 2026, Potash Imports Remain Critical

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