
Empowering sustainable agriculture with real-time crop intelligence and water stewardship.
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Arable achieves market-leading accuracy through a machine learning system that continuously improves by tapping into an extensive calibration and validation (Cal/Val) network. This network generates over 50 million data points quarterly, calibrating and validating the system against gold-standard research-grade instruments at more than 50 research sites globally, spanning diverse agricultural climate zones. This results in 350% more accurate data in critical frost ranges compared to standard gridded weather, and more granular, seamless data delivery than satellite imagery.
The Arable Mark sensors natively capture a wide range of environmental and plant data including temperature, rainfall, pressure, relative humidity, solar radiation, wind, vapor pressure deficit, dew point temperature, sunshine duration, sea level pressure, crop images, evapotranspiration (ETo and ETc), growing degree days (GDD), growth stages, leaf wetness, crop water balance, crop water deficit, canopy temperature, heat stress, NDVI, Kc-NDVI, chlorophyll index, and chill hours. Auxiliary sensors are required for detailed soil moisture, soil temperature, soil salinity, applied irrigation, and precise irrigation run times.
Arable provides a comprehensive API, allowing for the integration of its real-time data, forecasts, alerts, and images with other agricultural platforms and systems. This enables users to incorporate Arable's insights into their broader operational workflows and existing infrastructure.
Arable equips enterprises with reporting and data capabilities that enable them to quantify water savings and demonstrate progress towards their water goals. By providing real-time access to in-field rainfall, crop water use, and irrigation data, the system allows for measurable improvements to watershed health and farmer productivity, which can then be aggregated and reported at scale.
The Cal/Val (Calibration/Validation) network is a global system established through partnerships with over 30 leading research institutions. It involves continuously calibrating and validating Arable's system against gold-standard, research-grade reference instruments at more than 50 research sites. This network is crucial because it feeds over 50 million data points quarterly into Arable's machine learning models, ensuring the continuous improvement and market-leading accuracy of all measurements provided by the system.
Source: arable.com