By Lukie Pieterse, Potato News Today

How potato growers around the world are blending intuition with yield maps, storage sensors and weather tools to make sharper decisions without losing the human touch.

Global potato production has never been more dependent on both human judgement and hard numbers. While the total area planted to potatoes has fallen in recent years, global output has continued to rise, with higher yields driven by better genetics, agronomy and storage practices – and increasingly by a quiet, steady wave of digital tools reaching into potato fields and storages worldwide.

This is not a story of farmers abandoning their instincts. It is, rather, about growers in North America, Europe, Asia, Africa and beyond using yield maps, storage sensors and weather-driven decision tools as a second opinion – a way to sharpen decisions they once made entirely by feel.

The potato sector’s data moment

Potatoes are a classic candidate for precision and digital agriculture. They are high value, input intensive and very sensitive to soil, water and storage conditions. Researchers have long argued that these characteristics make potatoes especially suitable for more precise seeding, fertilisation, irrigation and crop protection, with the twin goals of higher revenue and lower environmental footprint.

At the same time, national and regional analyses of yield gaps show that even in advanced production regions, there is still room to lift output per hectare through better management. Studies in Europe, for example, have examined potato yield gaps in countries such as the United Kingdom, Denmark, Spain, Romania and Finland, highlighting how agronomic and climatic constraints still limit performance.

Digital tools are not a magic fix for those gaps – but they are rapidly becoming part of how growers diagnose problems, fine-tune inputs and keep crops and storages within tighter operating windows.

Yield maps in crops that were once ‘too awkward’ for sensors

For a long time, root crops lagged behind cereals in yield mapping. The physics of lifting potatoes and the complexity of harvester elevators made it difficult to get accurate, georeferenced flow readings. That is changing.

In parts of Europe, machinery dealers are now offering retrofitted yield mapping systems that can be installed on existing potato and sugar beet harvesters. These systems use load cells, optical sensors and GPS to generate yield maps that show how different parts of a field perform by variety, soil type or management zone.

Once in place, yield maps become the backbone for variable-rate strategies:

• Growers and agronomists can overlay yield data with soil maps, satellite imagery and elevation models to redesign management zones for future seasons.
• Variable-rate fertiliser and irrigation plans can then be built from those zones, targeting nitrogen and water to where they provide the most return, and reducing applications where response is limited.

In North America, South America and parts of Oceania, remote sensing from satellites and drones is being integrated into potato crop models, allowing tools to generate variable-rate maps that adjust mid-season when canopy development diverges from expectations.

Yet even the most tech-forward growers rarely hand full control to algorithms. Many still talk about checking maps against what they see and feel in the field – digging plants, smelling the soil, walking problem patches. The map starts the conversation; the spade and the farmer’s eye finish it.

From blight alerts to in-field decisions on a smartphone

Late blight remains one of the most economically significant diseases in global potato production. Over the past two decades, weather-driven decision support systems (DSS) have evolved from research curiosities to everyday tools in several regions.

Today, growers in parts of France for example use web-based DSS platforms that combine weather data and crop development models to recommend fungicide timings and intervals.

In North America, late blight DSS tools developed by universities and extension networks help farmers in the United States and Canada time sprays based on local weather station data.

Across Europe and neighbouring regions, regional platforms run late blight risk models fed by high-resolution weather data, with growers able to connect private weather stations and view risk on interactive maps.

In India and other Asian countries, government-funded projects have built web-based and app-based late blight tools that translate weather inputs into simple spray advisories, specifically designed to be usable by farmers with limited digital experience.

More broadly, national and regional potato disease decision tools now include:

• Dashboards that publish station-level disease risk indices.
• Commercial platforms that integrate multiple disease models and deliver alerts as app notifications, emails or SMS.

Accuracy is improving as models are recalibrated to local conditions and as more historical data accumulates. But the final call still rests with the grower, who weighs the forecast against their knowledge of variety resistance, field history, product cost and labour availability.

Smarter storages: sensors, airflow models and CO₂ as a management signal

The digital shift is perhaps even more visible behind closed doors in storages than in open fields.

Modern potato storages increasingly rely on continuous monitoring of temperature, relative humidity and carbon dioxide (CO₂) levels to protect quality and reduce losses. Industry guidance has long stressed the importance of appropriate sensor placement and control systems for air distribution and temperature management.

Newer commercial monitoring systems place networked sensors throughout the store, sending real-time data on temperature, humidity and CO₂ to dashboards accessible on phones or laptops. These systems can trigger alarms if ventilation fails, if CO₂ levels spike or if localised hotspots suggest airflow issues.

Several practical insights are emerging worldwide:

• Elevated CO₂ during storage can negatively affect fry colour and increase the risk of dark defects in chips, which has led processors and equipment suppliers to invest in more precise CO₂ monitoring.
• IoT-based storage platforms are using sensor data to reduce unnecessary ventilation, saving energy while maintaining product quality, including with targeted CO₂ extraction units that operate only when gas levels exceed defined thresholds.
• Researchers in countries such as China and others are combining experimental storages with computational fluid dynamics (CFD) models to simulate and improve airflow, temperature and humidity distribution – giving engineers and growers design tools that were unavailable a decade ago.

Again, the technology does not replace the storage manager. It changes the way they work. Instead of walking the store mainly by feel, they may scan a dashboard first, then go straight to the bins or boxes where data suggests something is drifting out of range.

AI, machine learning and the next layer of potato analytics

Beyond field-level sensors and storage probes, a more advanced layer of analytics is emerging.

Machine learning models are being tested to predict regional potato yields and assess climate risk. Recent work on Prince Edward Island, Canada, for example, has used machine learning techniques to project future yield changes under various climate scenarios, while other studies have explored algorithms like random forests and gradient boosting for crop yield prediction in broader settings.

In parallel, research teams in Canada have demonstrated the use of portable spectrophotometers and AI models to estimate petiole nutrient levels in the field. The goal is to allow farmers to diagnose nutrient status more quickly and adjust fertiliser rates in-season, with early results indicating that such tools can complement conventional tissue testing.

On the commercial side, several platforms are positioning themselves as end-to-end data hubs for potato production, using drone imagery, geospatial analytics and AI-driven diagnostics to monitor crop health, yield potential and soil moisture.

These tools remain in early adoption, often concentrated in large commercial operations and research collaborations. But they point toward a future where growers can query a field or storage the way they might query a search engine today – asking not only what is happening but why and what if.

Data as a second opinion – not a replacement for intuition

Across continents, one theme keeps resurfacing in interviews and case studies: growers are more willing to experiment with digital tools when the technology is framed as a support, not as an automated decision maker.

Several factors shape this attitude:

• Many older and younger farmers alike see value in decades of local experience – patterns of late blight development, the way a particular soil type responds to heavy rain, or how a specific variety behaves in storage.
• Weather models, yield maps and risk indices can be wrong when pushed beyond their calibration range, especially in extreme seasons. Growers who understand their limitations are more likely to keep using them.
• The cost and complexity of some systems mean that the first year is often about learning how to interpret the outputs rather than about immediate profit.

In practice, this often looks like a simple routine: a grower checks a late blight risk app in the morning, cross-checks it with their own reading of the sky, soil moisture and crop canopy, and then calls their agronomist. Data, field observation and professional advice blend into one decision.

This blend is not unique to high-tech regions. In parts of Asia, Africa and Latin America, SMS-based advisory services and basic smartphone apps are playing a similar role for smallholders, even where formal precision agriculture infrastructure is limited.

Barriers to adoption – and why the ‘quiet shift’ language matters

Despite the evident benefits, adoption of digital tools in agriculture remains uneven. Analyses in countries such as Canada, for example, suggest that while available tools have been shown to increase productivity and reduce environmental impact, overall uptake remains relatively low compared with some peer countries.

Barriers include poor rural connectivity, unclear return on investment and concerns around who controls and uses farm data.

Surveys in other regions echo similar constraints:

• Limited broadband or mobile coverage restricts the use of cloud-based platforms, particularly in parts of Africa, Latin America and Central and Eastern Europe.
• Growers are wary of sharing detailed yield maps and input records with suppliers, processors or tech providers without clear agreements on privacy and data ownership.
• In some smallholder systems, the immediate priorities of access to seed, finance and basic storage infrastructure make advanced digital tools a lower priority.

This is why the shift is still relatively quiet. It is happening field by field, storage by storage – a weather station here, a yield monitor there, a storage dashboard in one facility, a late blight advisory service in another. The transformation is incremental rather than dramatic.

Ensuring smaller growers and emerging regions are not left behind

A key question for the global potato community is whether digital agriculture will widen or narrow existing gaps between well-capitalised growers and those with fewer resources.

Encouragingly, several initiatives take a more inclusive path:

• Public DSS platforms for late blight and other diseases, funded by governments or research institutes, are offered free of charge to growers, with simple interfaces and regional training programmes.
• Low-cost sensor kits and open-source data platforms are being tested in developing regions, with the aim of making basic storage monitoring and field weather data accessible to small groups of growers.
• International research collaborations are exploring machine learning and yield gap analysis in ways designed to inform public policy and extension, not just commercial services.

However, without continued investment in connectivity, training and advisory support, there is a risk that the most advanced digital tools will cluster in a relatively small part of the global potato area.

What comes next – and what will not change

Analysts of digital agriculture estimate that, across crops, the widespread adoption of proven digital tools could unlock significant additional net farm revenue annually in major producing countries, while also reducing environmental impacts. It is reasonable to assume that potatoes, as a high-value crop under climate and market pressure, will account for a meaningful share of that potential.

Looking ahead, several trends seem likely:

• Integration of field, storage and processing data into single platforms, so that decisions made at planting, during irrigation or at harvest can be directly linked to storage performance and factory quality outcomes.
• Wider use of AI to flag anomalies rather than to prescribe actions – for instance, highlighting areas of a field where canopy development diverges from historical patterns, or bins in which temperature trends are subtly different from the rest.
• Continued refinement of disease and weather risk models as more seasons of data and more diverse environments are incorporated.

What will not change is the central role of the farmer’s judgement. Even in the most digitised operations, someone still has to decide whether a model’s recommendation makes sense for that field, that storage, that day.

In that sense, the potato sector’s digital story is not about replacing gut feel. It is about turning instinct and experience into a sharper, more resilient decision-making system – one where a grower in Canada, Kenya, India, the US or Ireland can move from gut feel to good data without losing the human wisdom that built the industry in the first place.

Author: Lukie Pieterse, Potato News Today
Image: Credit Potato News Today