B2B platform of IoT sensors and predictive analytics for agribusiness in Argentina. Anticipate your crop yield with real-time data.
Request a demoMeasurable results that the S.I.L.D.E.N. system delivers to your operation.
Anticipate the harvest with models trained on IoT sensor and satellite data. In soybeans and corn, we achieve 94% accuracy in final estimation.
Predictive maintenance based on machine learning reduces unplanned sensor interruptions by 40%. Fewer field visits, more continuous data.
We combine NDVI images with real-time ground readings. Biomass estimation error is reduced by 18% compared to using a single source.
Soil moisture, temperature, and nitrogen are measured every 15 minutes. The platform alerts on critical changes before they affect the crop.
Sensor data is sent directly to the tractor cab or management platform. No manual steps, no delays.
Designed to operate from 50 to 10,000 hectares. The sensor network adapts to topography and crop type without complex recalibrations.
Producers and companies trust Sildenafiled to monitor their crops with precise data.
Based on 120+ verified reviews from producers
“We implemented IoT sensors on 200 hectares of soybeans. The yield prediction helped us plan the harvest weeks in advance. Reliable data and excellent technical support.”
Carlos Méndez
Agricultural producer, Córdoba
“Integrating satellite data with field sensors gave us a more complete view of the corn's condition. We reduced manual inspection costs and improved the accuracy of our models.”
Laura Fernández
Agronomist engineer, Buenos Aires
“The predictive maintenance system prevented unexpected downtime in the middle of the season. The sensors worked without interruptions and the dashboard is very intuitive.”
Gustavo Ríos
Operations manager, Santa Fe
Features designed to help field teams and technical offices make decisions with concrete data.
Continuous reading of humidity, temperature, and nutrients from IoT sensors deployed in the field. Data is updated every 15 minutes and displayed on a per-plot dashboard.
Models trained with historical data from the Pampas region that estimate crop yield with up to 94% accuracy. Ideal for planning harvest and logistics.
Automatic notifications for sensor anomalies, sudden humidity changes, or frost risk. Each alert includes the exact sensor location and the affected variable.
Fusion of Sentinel-2 NDVI images with ground sensor data. This combination reduces biomass estimation error by 18% compared to using a single source.
Machine learning algorithms that detect wear patterns in sensors. In field tests, unplanned interruptions were reduced by 40% and device lifespan was extended.
Download historical series in CSV and JSON for external analysis. Compatible with BI tools and agricultural management platforms already used in your company.
Technical articles and use cases for the connected field
How field data transforms the harvest
Continuous monitoring of moisture and nutrients with IoT sensors to anticipate soybean yield in the Pampas region.
Read articleFusion of NDVI images with IoT readings
Combining Sentinel-2 satellite imagery with ground sensors reduces biomass estimation error by 18%.
Read articlePreventing field failures with machine learning
Anomaly detection algorithms reduce unplanned interruptions in Santa Fe sensors by 40%.
Read article