nquiringminds have developed a suite of agritech sensors that are capable of gathering key agricultural harvest data metrics and deploying state of the art machine learning to drive efficiency, energy and environmental gains. In agriculture our sensor technology works well at giving information from inaccessible places.  The technology supports open source and IOT standards for maximum interoperability with existing plant and machinery.  We had to make sure that the grain was kept fractionally below the acceptable moisture level, to assure maximum profitability for the estate, and our sensors needed to be robust enough to cope with the harsh farming environment.

By applying real time sensors and machine learning it is possible to confidently dry the grain to just below the threshold. And sell the grain for several pounds a tonne more than would otherwise be possible. With one grain dryer processing thousands of tonnes a harvest the additional revenue can quickly add up. The value we brought to the project was knowing how to operate an IOT network in extreme conditions of up to 100 degrees centigrade and then apply machine learning and real time responses to a dynamic system. As with everything we do the platform is hardware neutral and so can be easily integrated onto any existing equipment.

The agritech sensors cover all aspects of a crop life cycle: sowing, growing, harvesting, drying, storage and delivery.

Sensor Requirements:

  • Harsh environment – outside in the field, wind and rains resistant as well as in an industrial drier at temperatures up to 100oC
  • Robust transmission – both long range, weak field-to-farm and short noisy industrial deployments.
  • Seasonal deployments – with sensors required to operate for up to one year without battery change.

Interactive cloud based visualizations, analytics and control systems, will drive efficiency, reduce energy consumption and decrease the environmental impact of the harvest.

nquiringminds automates data collection through sensors and provides the tools to allow scarce expert knowledge to be shared across multiple simultaneous harvests; this is done through a mixture or efficient remote expert intervention and transfer of “expert knowledge” into honed machine learning systems.

The solution’s sensor hubs are be based on the ST 32 EuroCPS platform. Which possesses the right combination of low power, robust environmental performance and flexible integration options.