In commercial potato production, critical decisions around contracting, labour planning and supply allocation are made months before harvest. These commitments are built on assumptions that early crop development will proceed on schedule and deliver expected yields. As acreage expands and weather becomes more volatile, relying on those assumptions with limited early-season insight introduces significant risk.
Presia steps into this picture with a clear promise of certainty. The company delivers decision-grade intelligence on crop development, yield and tuber size early enough to predict outcomes. By combining satellite imagery, weather data and proprietary models into a continuously updated intelligence pipeline, Presia replaces intuition, fragmented scouting and late-season digs with a consistent, field-level view of performance and results. This allows growers and buyers to make decisions with intent rather than reaction.
“We’re all about potatoes,” says Tyler Hennick, managing director. “That crop specificity is a big part of what makes us unique and a true reflection of what we do.”
With expectations established earlier, teams can plan labour more accurately, direct field activity with purpose, meet supply commitments with confidence and manage operational risk as conditions evolve through the season.
Predictive Intelligence Built Specifically for Potatoes
Presia’s predictive models are trained on more than a decade of potato canopy and yield data spanning five continents and over sixty varieties, forming one of the deepest and cleanest potato-specific datasets in the world. This depth of data allows Presia to account for regional growing environments, climate behavior and varietal response with a level of precision that generalized crop models cannot match.
The system is designed to stay reliable as conditions change. Presia retrains its models after each growing season, incorporating observed weather patterns into the process. Conditions such as drought in eastern Canada and extreme weather events in the Pacific Northwest are added to the data as they occur, expanding the range of variability the platform can account for over time rather than relying on static assumptions.
Equally important, Presia ensures its intelligence appears where decisions are already being made. Instead of requiring teams to log in to a separate platform, the system integrates directly with existing farm management systems, internal databases, and custom dashboards. This allows insights to sit alongside operational data, embedding intelligence into daily workflows rather than treating analysis as a standalone task.
Establishing a Reliable Baseline for Field-Level Decisions
Reliable forecasting depends on defining a consistent point at which crop growth begins. In practice, growth starts at emergence rather than at planting, but it does not occur uniformly across fields or regions and is rarely verified consistently. As operations grow, confirming emergence through physical field visits becomes inefficient and uneven, making it harder to align early-season analysis and decisions across large fields.
Presia eliminates that uncertainty by remotely detecting crop emergence across every field, establishing a consistent biological starting point for prediction. From emergence onward, the platform tracks canopy development throughout the season, enabling direct field-to-field comparison and surfacing meaningful differences early.
With this visibility, teams can distinguish between fields that are progressing as expected and those beginning to diverge. Attention can be directed where it is most needed, allowing corrective action while adjustments still have a practical impact rather than reacting after issues emerge through late-season sampling.
Guiding Field Action, Not Replacing It
Presia is designed to sharpen field decision-making, not eliminate it.
By identifying which fields warrant attention and when, the platform helps teams prioritize visits strategically. When sampling is required, Presia further supports decision-making by highlighting representative zones within each field, reducing the risk of misleading observations.
This approach addresses a weakness in traditional digging programs, where samples taken near roads, low-yielding patches or other unrepresentative areas can distort expectations. Presia’s satellite- driven analysis guides sampling toward zones that more accurately reflect overall field performance.
“We make field visits more effective, so growers and agronomists spend their time where it makes the greatest difference,” says Hennick.
Built to Scale Across Regions and Seasons
Scalability of crop monitoring has been a design requirement from the beginning. Presia’s early development relied on high-resolution imagery from drones, aircraft and field-mounted cameras to establish dependable ground truth. While these methods were essential for building accuracy, they were costly and difficult to deploy across large geographies.
That foundation was later used to train satellite-based models capable of operating across regions and continents without on-farm effort. To address cloud interference during key growth stages, Presia incorporated radar-based satellite data that can see through cloud cover, ensuring continuity even in challenging conditions. Ongoing investment in data cleaning, normalization and validation protects accuracy as datasets expand globally.
As volatility becomes a defining feature of modern agriculture, the ability to translate early signals into practical guidance becomes decisive. Presia turns complex, field-level data into decision-ready intelligence that supports agronomic, operational and commercial planning throughout the season.
Presia steps into this picture with a clear promise of certainty. The company delivers decision-grade intelligence on crop development, yield and tuber size early enough to predict outcomes. By combining satellite imagery, weather data and proprietary models into a continuously updated intelligence pipeline, Presia replaces intuition, fragmented scouting and late-season digs with a consistent, field-level view of performance and results. This allows growers and buyers to make decisions with intent rather than reaction.
“We’re all about potatoes,” says Tyler Hennick, managing director. “That crop specificity is a big part of what makes us unique and a true reflection of what we do.”With expectations established earlier, teams can plan labour more accurately, direct field activity with purpose, meet supply commitments with confidence and manage operational risk as conditions evolve through the season.
Predictive Intelligence Built Specifically for Potatoes
Presia’s predictive models are trained on more than a decade of potato canopy and yield data spanning five continents and over sixty varieties, forming one of the deepest and cleanest potato-specific datasets in the world. This depth of data allows Presia to account for regional growing environments, climate behavior and varietal response with a level of precision that generalized crop models cannot match.
The system is designed to stay reliable as conditions change. Presia retrains its models after each growing season, incorporating observed weather patterns into the process. Conditions such as drought in eastern Canada and extreme weather events in the Pacific Northwest are added to the data as they occur, expanding the range of variability the platform can account for over time rather than relying on static assumptions.
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We’re all about potatoes. That crop specificity is a big part of what makes us unique and a true reflection of what we do.
Equally important, Presia ensures its intelligence appears where decisions are already being made. Instead of requiring teams to log in to a separate platform, the system integrates directly with existing farm management systems, internal databases, and custom dashboards. This allows insights to sit alongside operational data, embedding intelligence into daily workflows rather than treating analysis as a standalone task.
Establishing a Reliable Baseline for Field-Level Decisions
Reliable forecasting depends on defining a consistent point at which crop growth begins. In practice, growth starts at emergence rather than at planting, but it does not occur uniformly across fields or regions and is rarely verified consistently. As operations grow, confirming emergence through physical field visits becomes inefficient and uneven, making it harder to align early-season analysis and decisions across large fields.Presia eliminates that uncertainty by remotely detecting crop emergence across every field, establishing a consistent biological starting point for prediction. From emergence onward, the platform tracks canopy development throughout the season, enabling direct field-to-field comparison and surfacing meaningful differences early.
With this visibility, teams can distinguish between fields that are progressing as expected and those beginning to diverge. Attention can be directed where it is most needed, allowing corrective action while adjustments still have a practical impact rather than reacting after issues emerge through late-season sampling.
Guiding Field Action, Not Replacing It
Presia is designed to sharpen field decision-making, not eliminate it.
By identifying which fields warrant attention and when, the platform helps teams prioritize visits strategically. When sampling is required, Presia further supports decision-making by highlighting representative zones within each field, reducing the risk of misleading observations.
This approach addresses a weakness in traditional digging programs, where samples taken near roads, low-yielding patches or other unrepresentative areas can distort expectations. Presia’s satellite- driven analysis guides sampling toward zones that more accurately reflect overall field performance.
“We make field visits more effective, so growers and agronomists spend their time where it makes the greatest difference,” says Hennick.
Built to Scale Across Regions and Seasons
Scalability of crop monitoring has been a design requirement from the beginning. Presia’s early development relied on high-resolution imagery from drones, aircraft and field-mounted cameras to establish dependable ground truth. While these methods were essential for building accuracy, they were costly and difficult to deploy across large geographies.
That foundation was later used to train satellite-based models capable of operating across regions and continents without on-farm effort. To address cloud interference during key growth stages, Presia incorporated radar-based satellite data that can see through cloud cover, ensuring continuity even in challenging conditions. Ongoing investment in data cleaning, normalization and validation protects accuracy as datasets expand globally.
As volatility becomes a defining feature of modern agriculture, the ability to translate early signals into practical guidance becomes decisive. Presia turns complex, field-level data into decision-ready intelligence that supports agronomic, operational and commercial planning throughout the season.
