Industry Applications

IoT Analytics for Agriculture

Enable real-time decision making with predictive analytics and embedded AI.

Business Challenge

Many agriculture, animal health and consumer goods companies are still finding industrial IoT adoption a challenge, not knowing where to start or which automated processes will prove to be most advantageous. But there's tremendous potential to transform the way new products are discovered, developed, manufactured and commercialized. And with expectations of having more connected devices in the future, agriculture leaders can't afford to leave such a massive network untapped – particularly given the potential for greater efficiency and proactive instead of reactive interventions.

How IoT Analytics From SAS Can Help

Manage and analyze your IoT data and edge analytics where, when and how it works best for your business. Understand which data is relevant so you'll know what to store and what to ignore. SAS put trusted, automated IoT analytics solutions right at your fingertips so you can:

  • Measure in-field conditions. Access and analyze all types of crop field data – e.g., light, humidity, temperature, soil moisture. Then integrate that data into your automatic watering process for more efficient resource usage.
  • Collect and analyze agricultural drone data. Ground- and aerial-based drones can provide a wealth of information on agriculture crop health assessment, crop monitoring, crop spraying and more.
  • Monitor livestock wellness. Automated monitoring, tracking and reporting save time and money by helping you focus on critical animal health and well-being issues.
  • Drive research. Make data-driven decisions that speed research and development of new tools, technologies and products for the agriculture sector.
  • Assess sustainability. IoT sensors can measure energy consumption, carbon dioxide and water usage on the field, in the lab or on the manufacturing line.

Why SAS?

  • Enterprise-quality data management. Integrate structured and unstructured quality-related data from all sources to get a comprehensive view of quality performance and drive improved quality outcomes.
  • Superior root-cause analysis. Take advantage of a complete spectrum of analytical tools – from exploratory analysis, to design of experiments with optimizers, to cause-and-effect tools.
  • Advanced early-warning analytics. Identify potential issues early, even before they occur, so you can proactively take corrective action to improve outcomes.

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