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BtM, a Low-cost Open-source Datalogger to Estimate the Water Content of Nonvascular Cryptogams

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Loom: A Modular Open-Source Approach to Rapidly Produce Sensor, Actuator, Datalogger Systems.

William Richards¹, John Selker¹, Chet Udell¹

¹OPEnS Lab, Oregon State University, Corvallis, OR 97330, USA.

Sensors (Basel, Switzerland)

|June 19, 2024

Summary

This summary is machine-generated.

Loom is an open-source development environment for low-power microcontrollers, enabling rapid prototyping of environmental sensor systems. It supports diverse components and applications, accelerating research and STEM education in environmental monitoring.

Keywords:

4G LTE Arduino Loom Maker community OPEnS Oregon State University STEM education Wi-Fi agricultural monitoring atmospheric monitoring customizable sensors datalogging environmental sensing in situ sensors internet connectivity low cost low power microprocessors off-the-shelf sensors open-source power conservation radio telemetry real-time data scientific monitoring soil monitoring water monitoring

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Area of Science:

Environmental Science
Computer Engineering
Educational Technology

Background:

Environmental monitoring is critical due to population growth, climate change, and resource depletion.
Satellite data requires on-the-ground validation from in situ sensor systems.
Open-source tools are needed for rapid prototyping of environmental datalogging systems.

Purpose of the Study:

Introduce Loom, a development environment for low-power Arduino-programmable microcontrollers.
Facilitate rapid advancement of environmental research and monitoring programs.
Provide a modular and extensible platform for diverse sensing applications.

Main Methods:

Developed Loom as an integrated environment for microcontrollers.
Incorporated components for sensors, datalogging, internet connectivity (Wi-Fi, 4G LTE), and power conservation.
Designed for modularity, reconfigurability, and extensibility.

Main Results:

Loom reduces development time for environmental sensing systems.
Enhances efficiency through shared bug fixes and optimizations across projects.
Supports multidisciplinary teams in diverse applications including water, soil, atmosphere, agriculture, and education.

Conclusions:

Loom empowers developers to create custom environmental sensing applications efficiently.
The modular approach accelerates scientific monitoring and STEM education.
Loom offers a flexible solution for various environmental data acquisition needs.