Metaspectral’s AI platform uses hyperspectral imaging, edge computing to transform space, recycling and other industries
Vancouver-based Metaspectral has developed an AI platform that uses novel data compression and deep learning algorithms to analyze hyperspectral images in real time. According to the company, this technology not only has the potential to transform multiple industries such as agriculture and materials recycling but is also being deployed in space.
The hyperspectral imaging enabled by Metaspectral’s platform captures data across the entire electromagnetic spectrum. It allows computer vision, for example, to identify materials and gasses at the molecular level in real-time. In the space industry, this technology is being used by the Canadian Space Agency to measure greenhouse gas levels on Earth.
Humans can view three distinct color bands ranging from 450 to 650 nanometers, red, green and blue. However, hyperspectral imaging can detect 300 individual wavelengths, capturing these spectral values digitally for further use.
According to the latest research by Emergen Research, the global Hyperspectral Imaging (HSI) system market size is expected to grow at a CAGR of 18.5% and reach a value of USD 70.7 billion in 2030. Hyperspectral imaging’s industrial applications are a crucial factor powering revenue growth in this industry.
For example, hyperspectral technology can help automate industrial systems for food safety checks and detect foreign objects without physical contact. This technology also allows users to categorize items accurately while sorting or grading materials precisely.
While the market is currently growing, there are challenges as well. For example, these cameras generate a significant amount of data.
“Hyperspectral cameras capture data at about a gigabit per second, so very high data throughput, because they capture up to 300 wavelengths of light,” Migel Tissera, the Metaspectal CTO and co-founder, told EdgeIR. “We saw the need for efficient data handling for hyperspectral cameras.”
That said, Tissera explained that it is very difficult to increase bandwidth when it comes to space.
“Governments give licenses to the RF spectrum, which is the radio frequency spectrum, and a satellite operator might get allocated some slice of that, and they have to make use of that allocated spectrum,” explained Tissera. “If you use cameras like hyperspectral, which use a lot of data, you only have two options. You either increase the bandwidth or use some efficient compression to reduce that data.”
Metaspectral’s AI platform aims to solve this issue by promising a compression ability of up to 90%. Its technology is slated for use on the International Space Station to provide real-time data compression, streaming and analysis of hyperspectral data from Low Earth Orbit (LEO).
Using hyperspectral imaging to sort recycling…and more
The recycling industry also benefits from the use of hyperspectral imaging. As an illustration, Metaspectral engineered a system that leverages edge computing to accurately distinguish plastics based on their chemical structure for real-time sorting.
According to Metaspectral, traditional sorting methods failed when it came to transparent or black plastics, and this technology was able to resolve the issue.
The platform is also used in agriculture. Metaspectral says its technology enables farmers to use hyperspectral imaging and machine learning algorithms to detect diseases early before the human eye can see it.
“There is a very specific spectral signature in Botrytis, a common greenhouse fungal disease and you can pick it up using hyperspectral analysis,” said Tissera.
He further explained that Metaspectral’s technology is making its mark in different areas, including climate change mitigation. Besides helping to solve the plastic recycling issue, he stated that the technology could detect wildfires, for example.
“With hyperspectral analysis, we can quantify the available fuel, so to speak, if there was ever a spark,” stated Tissera. “So dry mass, vegetation, grass … all contribute to wildfires. We analyze and output heat maps overlaid in any given area to highlight the dry vegetation. So you can take proactive measures like controlled burns, for example.”
Metaspectral secured $4.7 million in seed funding from SOMA Capital, Acequia Capital, the Government of Canada and individual investors Jude Gomila and Alan Rutledge in late 2022. The company is actively hiring and has a new recycling product, developed alongside a customer, hitting the market this year. He also stated that the company would release a new cloud platform this year.
“Here, you can save all of your data, analyze all of your data, create deep learning models and then those DL models can be deployed to the edge,” added Tissera. “It’s a circular system that we have.”
Analysis
According to Carmi Levy, a technology analyst and communications director at Step Software Inc, Metaspectral’s technology is groundbreaking and arrives at a time when the world needs this insight more than ever.
“Hyperspectral image analysis gives scientists, researchers, industries and governments the ability to see materials and gasses in ways we’ve never been able to see”, Levy explained. “It’s no wonder the Canadian Space Agency is planning to leverage the platform to track greenhouse gas levels, because this capability represents something of a holy grail: a cost-effective, scalable, sophisticated means of precisely measuring the current and projected health of the atmosphere.”
Even better, the use cases for this technology extend well beyond atmospheric research, he continued. This technology has the potential to transform many industries while providing environmental advantages.
Mahmudul Hasan, a lead data scientist at Telus and an AI/ML instructor at the University of Toronto (School of Continuing Studies), concurs with this analysis:
“Hyperspectral image processing technology will become a powerful tool for environmental monitoring and management in different areas from helping to assess the health of ecosystems, to detect pollution and degradation to support sustainable use of natural resources in real-time and much more detail than what we can do today.”
Article Topics
computer vision | edge AI | hyperspectral imaging | LEO | Metaspectral | satellite | Smart Ag
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