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Towards an Efficient and Programmable Computer Vision System for High Throughput Livestock Monitoring

Project Team

  • Dr. Narendra Ahuja, Research Professor, Dept. of Electrical and Computer Engineering
  • Dr. Matthew Caesar, Associate Professor, Dept. of Computer Science
  • Dr. Ryan Dilger, Associate Professor, Dept. Of Animal Science
  • Dr. Angela Green-Miller, Associate Professor, Dept. of Agriculture & Biological Engineering

Abstract

Livestock monitoring is crucial for the effective functioning of modern farms, but is bottlenecked on the ability to sense, record and analyze enormous volumes of information at many scales from molecular to cellular to subsystem to entire animals and animal populations. Computer vision is widely viewed as a key technology to relieve this bottleneck, though it still poses significant challenges for efficient data transfer and handling. In this work, we propose a programmable computer vision system that can greatly improve the scalability of livestock monitoring, by solving several key challenges. First, we will develop a highly scalable computational infrastructure for computer vision that can process the massive volumes of sensor data by distributing the processing between the edge (near the sensors) and the cloud. The edge compute hardware will leverage emerging heterogeneous systems that include special-purpose accelerators. Second, we will develop a Livestock Query Language (LQL) to allow non-programmer agricultural researchers an intuitive and expressive way to pose queries and evaluate hypotheses across large image and sensor data sets. Third, we will develop novel compilation and optimization techniques to translate and execute queries on the underlying heterogeneous hardware. Fourth, we will develop a livestock data storage system that can store, manage and access data from diverse settings and heterogeneous sensor data sources, which often pose a major practical hurdle for sensor-intensive agriculture research. In future work, we will incorporate this programmable computer vision system into the digital farm infrastructure (IoT for Agriculture) that is a key component of the Center for Digital Agriculture vision, and collaborate with other agriculture researchers to deploy and evaluate these technologies in diverse experimental settings. For specific applications, we will begin with evaluation, framework development, and deployment solutions in pig production systems, with a broader vision to test this framework across other production systems.