EU project Hyperimage developing hyperspectral camera

05 Nov 2024

Paired with AI and drones, system could boost farm yields by 20%, while cutting costs, and producing less waste.

A team of EU scientists is developing a novel type of camera with the aim of transforming various industries, including vertical farming.

The hyperspectral camera will, for example, allow farmers growing crops like salads, herbs, and microgreens to detect plant diseases early, monitor crop health with precision, and optimise harvest times – boosting yields by up to 20%, say its developers.

A European consortium funded under the Photonics Partnership is developing a new imaging platform. Called HyperImage, the project aims to revolutionize quality assurance and operational efficiency across different sectors.

The imaging system uses AI machine learning algorithms to identify objects to enable more precise decision-making. The system is also expected to reduce farming costs, produce less waste, and generate up to 20% higher yields than traditional methods.

Beyond human vision

The announcement, from European photonics platform Photonics 21, states, “Unlike regular cameras that capture just red, green, and blue, hyperspectral cameras capture many different wavelengths of light from infrared to the ultraviolet regions, called the spectral dimension. Scientists can then analyse this spectral data in real-time with AI to classify different objects and materials, instantly and unequivocally spotting things like paint, pollutants, or diseases in crops.”

Alexander Kabardiadi-Virkovski, head of the HyperImage project at Fraunhofer IWS, commented, “The integration of hyperspectral cameras into autonomous systems represents a significant advancement in both technology and application. This imaging technique promises to enhance productivity and sustainability in industry and agriculture. Although hyperspectral imaging has existed since the 1980s, it has never been used in real-time with artificial intelligence for the applications the HyperImage team is now exploring.”

“Our new system is being developed for automated vertical farming but is simultaneously being used to improve quality control, navigation in off-road autonomous driving, and vision systems for unmanned geo-surveillance drones. The innovative HyperImage approach will enable autonomous vehicles to perform detailed landscape analysis and make informed decisions regarding passability in outdoor environments. By standardising hyperspectral data across different camera manufacturers, we aim to create a universal solution for object recognition and quality control.”

Surveillance drones

The HyperImage team is also working to improve the efficiency of a string of other use cases, including for surveillance drones. The photonic components and spectral imaging technology deployed in the HyperImage platform will eventually enable UAVs to perform longer missions with greater energy efficiency and improved operational effectiveness in surveillance and monitoring.

“Tasks that use UAVs, like environmental monitoring, infrastructure inspection, disaster management, and agricultural surveys, could all be improved by the HyperImage system. By using hyperspectral imaging, drones will be able to capture detailed data across several different wavelengths, allowing them to detect subtle changes in vegetation, identify materials, or monitor the integrity of infrastructure with much better precision than traditional sensors,” said Kabardiadi-Virkovski.

The team is working on a 10% reduction in drone weight (in the 25 kg maximum take-off weight class) by developing a lightweight, high-resolution hyperspectral vision system. This weight reduction creates additional space for a larger battery, which in turn boosts flight time by 50%.

The project, which will conclude in 2027, is funded by the European Union and brings together a consortium of twelve partners from industry and academia, which include 4K-MEMS, AMIRES, DIVE imaging systems GmbH, Growy, Infineon Technologies Bipolar, KETMarket GmbH, Netcompany-Intrasoft, Norsk Elektro Optikk AS, Optotune, Robotnik Automation, SILIOS Technologies, Stichting Wageningen Research, HySpex.