Use Case

Multi-Sourced Data Analytics to Improve Food Production & Security

K. McCurdy (Sedgwick Reserve, University of California, Santa Barbara Natural Reserve System) with B. Roberts, B. Sethuramasamyraja (California State University, Fresno), B. Liu (California State Polytechnic University, San Luis Obispo), C. Krintz, R. Wolski (UC Santa Barbara)


  • Interdisciplinary analysis of data from a wide variety of sources, including the general public, to study and improve ecological outcomes

Why Aristotle?

  • Scalable and portable infrastructure
  • Access to multiple data sources, many of which are already in public and private clouds


  • Completed large-scale Google TensorFlow training and parallel image classification runs on Aristotle for the “Where's the Bear?” camera trap application analysis project (20 training runs using 2000 cores hours per run and a classification run using 1800 core hours to classify a test sample of 10,000 images prior to classifying 240,000 images from a single camera).
  • Used soil moisture sensing devices, edge computing, and Aristotle to schedule vineyard irrigation (saved 66% of the water used previously).
  • Installed hardware and software to instrument almond trees in a Fresno, CA test orchard to see how much water can be saved by irrigating the different sides of the root stock in proportion to its dryness.
  • Developed innovative analytics with a minimally invasive instrumentation footprint for an Exeter, CA citrus test orchard to get highly accurate temperature readings at night when frost could form.


  • Develop sustainable land use practices at Sedgwick that employ livestock as part of the management lifecycle and install new monitoring infrastructure.
  • Deploy Phase 2 infrastructure at Lindcove in time for the citrus “frost season” and test new real-time analytics in a production farm context.
  • Analyze Phase 1 SmartFarm results and in spring 2019 deploy Phase 2 at scale in order to observe a full year cycle and analyze the impact of differential irrigation.


UCSB use case scientists are identifying wildlife images with machine learning

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