Short answer

Prioritize modularity and open-source components when designing specialized data acquisition systems to enhance affordability, adaptability, and accessibility for a wider range of users.

Field: Commercial Production
Source: Sensors (2023)
Method: System Development and Field Deployment
Evidence: Strong effect

An open-source, customizable data acquisition system built with commercial-off-the-shelf components can enable widespread, cost-effective deployment of hyperspectral imaging for remote sensing applications. This commercial production research insight is drawn from a 2023 study published in Sensors. Using System development and field deployment, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Prioritize modularity and open-source components when designing specialized data acquisition systems to enhance affordability, adaptability, and accessibility for a wider range of users.

Study

Commercial ProductionRecentStrong effect

Open-Source Data Acquisition System Enables Cost-Effective Hyperspectral Remote Sensing

Sensors · 2023

Key Findings

01A functional, open-source data acquisition system for hyperspectral imaging was successfully developed.
02The system enables concurrent collection of hyperspectral and navigation data, facilitating direct georeferencing.
03The entire system, including the imager and housing, weighed 735g, demonstrating a lightweight and deployable solution.
04The use of COTS components and open-source software resulted in a low-cost and customizable system.

Application

Design takeaway

Prioritize modularity and open-source components when designing specialized data acquisition systems to enhance affordability, adaptability, and accessibility for a wider range of users.

How to apply

When developing systems for data collection in specialized fields, consider integrating readily available microcontrollers, single-board computers, and open-source software to reduce development time and cost, while ensuring modularity for future upgrades.

Project actions

01Consider using a Raspberry Pi or Arduino for data logging and sensor integration in your design project.
02Explore open-source libraries for sensor communication and data processing.
03Document the bill of materials and assembly process thoroughly for reproducibility.

Method & Evidence

AimHow can a low-cost, lightweight, and deployable data acquisition system be developed using open-source hardware and software to concurrently collect hyperspectral and navigation data for direct georeferencing?

MethodSystem Development and Field Deployment

ProcedureThe researchers designed and assembled a data acquisition device using commercial-off-the-shelf hardware, including a microcontroller and a custom PCB. This device interfaces with ancillary navigation sensors and a Raspberry Pi 4B/NVIDIA Jetson. The system was integrated with an open-source hyperspectral imager and tested on a drone, collecting synchronized hyperspectral, navigation, and orientation data for georeferencing.

ContextRemote sensing, environmental studies, agriculture, forestry, geology, hydrology, drone technology

Variables

IV["Integration of COTS hardware and open-source software","System design for concurrent data acquisition"]

DV["Successful concurrent data collection (hyperspectral, navigation, orientation)","Georeferencing accuracy","System weight and cost"]

CV["Type of hyperspectral imager used","Drone platform","Environmental conditions during testing"]

Strengths & Limitations

Strengths

+Demonstrates a novel and practical solution to a known technological gap.
+Emphasizes cost-effectiveness and deployability through COTS and open-source choices.

Limitations

The cost-effectiveness is relative to professional systems; the initial investment in components might still be significant for some. The 'open-source' aspect primarily applies to the software and system design, not necessarily all hardware components.

Reliability & validity

Reliability would be assessed by repeated trials under similar conditions to ensure consistent data capture. Validity is supported by the successful georeferencing of hyperspectral data, indicating the system accurately captures and integrates necessary information.

Think critically

To what extent does the 'open-source' nature of this system truly democratize access, considering the specialized knowledge and potential cost of the hyperspectral imager itself?

Design Principles

"Leverage open-source ecosystems and commercial-off-the-shelf components to democratize access to advanced technological solutions."

This research demonstrates a practical approach to overcoming the barrier of specialized data acquisition hardware for advanced imaging technologies. By leveraging open-source principles and readily available components, designers can create more accessible and adaptable solutions for data collection in fields like environmental monitoring and agriculture.

What This Means for Your Design

This research shows how to build a cheap and easy-to-use system for collecting special camera data (hyperspectral imaging) and location data at the same time, using parts you can buy easily and free software, which is great for things like studying the environment from a drone.

How to use in your project

1.Reference this study when discussing the development of custom hardware or data acquisition systems for your design project, particularly if using COTS components or open-source software.

Add to My Project

Quick Cite

Paragraph starter

The development of a customisable data acquisition system, as demonstrated by Mao et al. (2023), provides a valuable precedent for creating accessible and cost-effective solutions. Their approach, utilizing commercial-off-the-shelf components and open-source software to integrate hyperspectral imaging with navigation sensors, highlights the potential for designers to overcome hardware limitations and enable wider adoption of advanced technologies in fields such as environmental monitoring.

Source

Sensors

A Customisable Data Acquisition System for Open-Source Hyperspectral Imaging

journal · 2023

View source

Questions About This Research

What does the research say about open-source data acquisition system enables cost-effective hyperspectral remote sensing?: Prioritize modularity and open-source components when designing specialized data acquisition systems to enhance affordability, adaptability, and accessibility for a wider range of users. Evidence: Sensors (2023).
Why does "Open-Source Data Acquisition System Enables Cost-Effective Hyperspectral Remote Sensing" matter for design?: This research demonstrates a practical approach to overcoming the barrier of specialized data acquisition hardware for advanced imaging technologies. By leveraging open-source principles and readily available components, designers can create more accessible and adaptable solutions for data collection in fields like environmental monitoring and agriculture.
How can designers apply this research?: Prioritize modularity and open-source components when designing specialized data acquisition systems to enhance affordability, adaptability, and accessibility for a wider range of users.
What were the main findings?: A functional, open-source data acquisition system for hyperspectral imaging was successfully developed.. The system enables concurrent collection of hyperspectral and navigation data, facilitating direct georeferencing.. The entire system, including the imager and housing, weighed 735g, demonstrating a lightweight and deployable solution.. The use of COTS components and open-source software resulted in a low-cost and customizable system.
What research method was used?: System Development and Field Deployment.
How strong is the evidence?: Evidence strength is rated Strong effect, based on a 2023 journal from Sensors.
What should I do differently in my next project?: When developing systems for data collection in specialized fields, consider integrating readily available microcontrollers, single-board computers, and open-source software to reduce development time and cost, while ensuring modularity for future upgrades.
What are the limitations?: The study focused on a specific hyperspectral imager (OpenHSI) and drone platform (Matrice M600); performance with different hardware configurations may vary. The long-term durability and reliability in diverse environmental conditions were not extensively detailed.