On-Farm Weather Stations: How X-Sense Helps Plan Treatments

A weather forecast pulled from a popular mobile app usually covers the nearest larger town, sometimes a dozen or more kilometers from the actual field. Yet microclimate conditions — ground-level temperature, leaf wetness duration after morning dew, or localized rainfall — can vary significantly even between neighboring plots, let alone across an entire region. An on-farm weather station installed directly in the field removes that uncertainty.

What a Weather Station Actually Measures

A station like X-Sense records several key parameters at once: air temperature and humidity, total rainfall, wind direction and speed, and leaf wetness duration. That last parameter is often underappreciated — many fungal pathogens need a certain number of hours of moisture on the leaf surface before an infection can begin. Without a leaf wetness sensor, that condition simply isn't visible, even though it's frequently the deciding factor in whether a fungicide application is worthwhile.

Data from the station is transmitted to the platform at regular intervals and shown in the AgroWeather module, where it's combined with weather forecasts to build a picture of both current conditions and what to expect over the coming days.

From Measurement to Treatment Decision

Temperature or humidity readings alone aren't yet an agronomic decision — only a model combining several parameters at once can assess the real risk. The AgroWeather module uses X-Sense data to build local risk models, for example conditions favorable to fungal disease development, or spring frost risk, which is dangerous for young plants or flowering orchards.

When such a model flags elevated risk, the Alerts module automatically sends a notification — push, SMS or email — before the situation becomes critical. This means the treatment decision is made ahead of time, not in a rush after the first disease symptoms appear on the leaves.

Fewer "Just in Case" Treatments

One of the most practical effects of having an on-farm weather station is the ability to reduce the number of preventive treatments applied "just in case" — because a neighbor sprayed, or because the calendar suggested a typical date. When the data shows that conditions don't favor development of a particular disease, the decision to hold off becomes easier to justify — including in the context of treatment documentation and growing requirements around the rational use of crop protection products.

On the other hand, when conditions do favor infection, station data helps choose the optimal time window for treatment — for instance avoiding a period right before forecasted rainfall that could reduce spray effectiveness.

Planning Other Field Operations

A weather station isn't only useful for assessing disease risk. Humidity and rainfall data also help plan other operations — when it's safe to bring machinery into the field (soil that's too wet increases the risk of compaction), the right window for harvest, or the timing of top-dressing fertilization, where upcoming rain can help wash fertilizer into the soil.

Location and Number of Stations Matter

On larger or more varied farms, a single centrally placed weather station may not be enough. A field lying in a valley near a watercourse will have a different microclimate — dew lingering longer, higher risk of radiational frost — than a field on a hilltop exposed to wind. In practice, it's worth considering placing several X-Sense stations at key points across the farm, particularly where disease- or frost-sensitive crops are grown, such as orchards or vegetable plantings.

Data from multiple stations, combined in the Field Monitoring module, also makes it possible over time to see which parts of the farm consistently fall into a higher-risk group — useful information when planning new plantings or rethinking crop structure.

A Practical Starting Point

For many farms, a weather station is the natural first piece of a rolled-out system — relatively simple to install, delivering tangible results already in the first season, and forming the data foundation that other parts of the ecosystem can build on, from the Alerts module to more advanced disease risk models, which we'll look at more closely in the next post on the xFarm blog.