Use cases, integrations, and real-world field maintenance.
How field data transforms the harvest
In the Argentine Pampas region, producers are adopting IoT sensors from Sildenafiled to measure key variables such as soil moisture, temperature, and nitrogen levels. This article analyzes a real case in the province of Córdoba where the sensor network allowed predicting the final soybean crop yield with 94% accuracy. Calibration challenges, integration with analysis platforms, and the impact on agronomic decision-making are discussed.
Read articleFusion of NDVI images with IoT readings
Sildenafiled has developed a pipeline that fuses Sentinel-2 satellite imagery with real-time IoT sensor data for corn crops in northern Buenos Aires. The article details the data architecture, spatial interpolation algorithms, and results from a 6-month campaign. An 18% reduction in biomass estimation error was observed compared to using sensors or satellites alone.
Read articlePreventing field failures with machine learning
IoT sensors in agricultural fields are exposed to dust, extreme humidity, and machinery vibrations. This article presents Sildenafiled's predictive maintenance system, which uses machine learning models to anticipate failures in humidity and temperature sensors. Based on data from 200 sensors deployed in the province of Santa Fe, the system managed to reduce unplanned interruptions by 40% and extended the average lifespan of the devices by 25%.
Read articleThe S.I.L.D.E.N. network integrates soil moisture sensors, ambient temperature sensors, solar radiation sensors, and complete weather stations. All devices are designed to operate in open field conditions in the Argentine Pampas region, with IP67 protection and autonomy of up to 18 months.
Raw sensor data is sent every 15 minutes to the cloud, where a machine learning pipeline combines real-time readings with NDVI satellite imagery and historical models of the area. The result is an estimated yield index per batch, updated every hour during the campaign.
Yes. The sensors locally store up to 72 hours of readings. When the device regains connectivity (cellular network or LoRaWAN), it automatically synchronizes the data with the control panel. This ensures continuity even in areas with intermittent coverage.
The predictive models are calibrated for soybeans, corn, and wheat in the provinces of Buenos Aires, Córdoba, and Santa Fe. We are expanding to sunflower and sorghum for the next campaign, with validation in fields of partner producers.
The platform exposes a REST API with endpoints to export performance data, alerts, and time series. Predefined connectors already exist for automated irrigation systems and input management platforms. Integration is configured in less than a week with our technical team.