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A Multimodal Sensing Platform for Interdisciplinary Research in Agrarian Environments.

James ReynoldsEvan WilliamsDevon MartinCaleb ReadlingParvez AhmmedAnders S HusethAlper Bozkurt
Published in: Sensors (Basel, Switzerland) (2022)
Agricultural and environmental monitoring programs often require labor-intensive inputs and substantial costs to manually gather data from remote field locations. Recent advances in the Internet of Things enable the construction of wireless sensor systems to automate these remote monitoring efforts. This paper presents the design of a modular system to serve as a research platform for outdoor sensor development and deployment. The advantages of this system include low power consumption (enabling solar charging), the use of commercially available electronic parts for lower-cost and scaled up deployments, and the flexibility to include internal electronics and external sensors, allowing novel applications. In addition to tracking environmental parameters, the modularity of this system brings the capability to measure other non-traditional elements. This capability is demonstrated with two different agri- and aquacultural field applications: tracking moth phenology and monitoring bivalve gaping. Collection of these signals in conjunction with environmental parameters could provide a holistic and context-aware data analysis. Preliminary experiments generated promising results, demonstrating the reliability of the system. Idle power consumption of 27.2 mW and 16.6 mW for the moth- and bivalve-tracking systems, respectively, coupled with 2.5 W solar cells allows for indefinite deployment in remote locations.
Keyphrases
  • data analysis
  • human health
  • solar cells
  • risk assessment
  • high throughput
  • life cycle
  • public health
  • air pollution
  • machine learning
  • healthcare
  • chronic pain
  • pain management
  • big data
  • deep learning