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Quantum Options debuts real-time water mapping payload at SPIE Future Sensing Applied sciences – sUAS Information


Q.Fly® Water drone payload delivers 400× greater decision than satellite tv for pc NDMI, enabling real-time moisture insights for land and infrastructure administration Oxford, United Kingdom, November 2025 – Quantum Options will current its new Q.Fly® Water system at SPIE Future Sensing Applied sciences in Yokohama, Japan, a dual-band drone payload that maps floor moisture in actual time and at as much as 400× finer spatial element than satellite-based strategies.

Utilizing quantum-dot shortwave infrared (SWIR) expertise, Q.Fly® Water captures near-infrared and SWIR knowledge concurrently to calculate a Normalized Distinction Moisture Index (NDMI) immediately onboard the drone. The system streams stay, color-coded moisture maps straight to the DJI flight controller, permitting customers to immediately determine areas of moisture variation throughout land, crops, and infrastructure.

Realizing the place water is – and the place it isn’t – is significant for managing these environments. Till now, that
perception has usually relied on delayed or low-resolution knowledge. Actual-time moisture mapping helps visualize
irrigation efficiency and areas of potential drought stress, giving fast suggestions on how water
strikes, collects, or recedes throughout the panorama. With rising international consideration on local weather resilience
and sustainable useful resource use, instruments that allow dynamic, high-resolution water sensing are important.
“Q.Fly® Water combines tuned quantum-dot SWIR sensors with onboard NDMI computation to ship a
stay moisture view with centimetre precision,” stated Alexander Bessonov, VP of Merchandise, Imaging Methods
at Quantum Options, who might be presenting the findings at SPIE. “It means choices can now be made
in actual time, proper from the controller display screen.”
The payload bridges the hole between coarse satellite tv for pc imagery and single-point soil sensors, offering
centimetre-level NDMI maps for purposes from precision agriculture and turf administration to wetland
monitoring and infrastructure inspection. Current in-field testing has targeted on agriculture and irrigation
and even archaeology.

“Advances in quantum dot SWIR imaging expertise, aerial robotics and on-edge computing are permitting us to disclose info that was beforehand invisible,” stated Marat Lutfullin, CEO of Quantum Options.

“Our mission is to make that hidden knowledge accessible and sensible, turning high-performance imaging into
an on a regular basis instrument for these managing important infrastructure.”

Developed and manufactured within the UK, Q.Fly® Water is constructed on Quantum Options’ proprietary
quantum-dot imaging platform, designed to increase imaginative and prescient past the seen spectrum. The corporate’s
light-weight, low-power sensors allow a brand new technology of UAV payloads for short-wave infrared
exploration, photo voltaic inspection, and distant sensing. Weighing round 600 grams, the payload integrates
with DJI Matrice drones and operates via the usual DJI Pilot interface, bringing lab-grade spectral
imaging into sensible subject use.

Alexander Bessonov will current the paper “Actual-time moisture mapping with a dual-band UAV payload”
at SPIE Future Sensing Applied sciences on 11 November 2025 at 14:40 Japan Customary Time, Pacifico Annex
Corridor, Room F203.

About Quantum Options

Quantum Options is a UK-based imaging expertise firm pioneering quantum-dot shortwave
infrared (SWIR) imaging programs that reach imaginative and prescient past the seen spectrum. Via full vertical
integration from quantum-dot supplies to finish UAV payloads and knowledge analytic platforms, the
firm makes superior spectral imaging sensible and accessible. Its Q.Fly® household of drone-based
sensing programs delivers new insights throughout vitality, surroundings, and industrial inspection. With
headquarters in Oxford and engineering in Cambridge, Quantum Options is making the invisible seen.


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