The first commercial deployment of hyperspectral AI in Canadian municipal agriculture — presymptomatic disease diagnostics delivered to Alberta's mandated Agricultural Service Boards, farmers, and agronomy partners. Flight data becomes field-specific prescription maps before visible symptoms appear.
The tools growers rely on today can detect that a plant is stressed, but not why. Disease, drought, nutrients, and insects look identical on an NDVI map — and by the time symptoms are visible to the eye, physiological damage has already occurred and pathogens have spread beyond the treatment window. Across every major growing region, the result is the same: producers manage losses instead of preventing them.
Manual scouting covers a fraction of total acreage and only detects disease after visible symptom expression — when yield damage has already begun. A scout walking 1,200 acres cannot provide field-wide coverage.
Accurate — but at $50–$150 per sample, cost-prohibitive for field-wide application. Results arrive on a multi-day lag, too slow to inform within-season management decisions at scale.
Standard drone and satellite imagery detects that a plant is stressed — but cannot identify why. Disease, drought, nutrients, insects all look identical. Critically, NDVI only flags stress after physiological damage is already underway.
Pixal 3D combines drone-mounted hyperspectral imaging, agronomic ground truthing, and machine learning to convert spectral signatures into disease-specific maps. The result is not another stress image. It is a decision layer for municipalities, agronomists, and growers.
Pixal 3D will perform drone based hyperspectral imaging over a field of interest.
The collected hyperspectral dataset is fed through our ML to detect clubroot. Our model can classify crops infected with clubroot with a 93% accuracy rate.
With a detailed field wide map of where disease pressures exist:
Pixal 3D captures reflectance information across visible and short-wave infrared wavelengths, expanding the signal available for disease discrimination well beyond standard RGB or multispectral capture.
Pixal 3D is fundamentally different from existing remote-sensing companies. We don't just flag that a plant is stressed — we identify exactly why, at the pre-symptom stage.
Competitor products surface generalized stress indices from multispectral imagery, without the spectral resolution to separate drought, nutrient, or disease causes.
Sub-centimetre spectral visibility distinguishes abiotic vs. biotic stress factors and resolves specific pathogens at the pre-symptom stage.
By the time visible stress appears on a competitor's NDVI map, yield damage has already begun and the treatment window is closing — leaving growers managing losses rather than preventing them.
Sub-centimetre hyperspectral signatures capture the biochemical shifts that precede visible symptoms — opening a window for targeted, preventive intervention before yield loss begins.
Competitor platforms rely on proprietary stress indices. None publish peer-reviewed disease-detection benchmarks because the underlying multispectral hardware cannot resolve individual pathogens.
Our detection methodology is grounded in a peer-reviewed hyperspectral ML study co-authored with Agriculture & Agri-Food Canada and University of Guelph researchers — the foundational science competitors don't have.
The global agrochemical industry sprays and fertilizes more every year — and still loses up to 40% of the harvest to pests and disease. Blanket chemistry can’t fix a diagnostic problem. Pixal 3D is the precision layer that makes existing agricultural spend work. Sized below, TAM → SAM → SOM, with Canada as the launch market.
Global agricultural drone & precision crop-management market by 2033, growing at 30%+ CAGR.
Underpinned by $220B in annual global plant disease losses and $250B in global agrochemical spend — inefficiency Pixal 3D converts into precision.
Canadian disease-scouting & precision-spraying service market once Pixal 3D's spectral library covers the country's major field crops — built on 93.6M acres of Canadian cropland across canola, wheat, barley, pulses & corn.
Inspection: 93.6M acres × $8/acre × 5 visits/season = $3.74B. Precision spraying: 35% of acres × $20/acre = $655M.
Today-capable wedge: canola's ~22M library-validated acres yield a $1.03B near-term launch market. Each additional crop added to the library expands the SAM.
Pixal 3D financial-model projection for 2032: 128 ASB contracts at $75K + 400K farmer acres + agrochemical commissions.
Alberta fully penetrated; Saskatchewan and U.S. entry underway.
Three compounding revenue engines, each activating in sequence
Pixal 3D already has the ingredients investors look for a category-creating ag-AI company: an active municipal and provincial deployment path, and an industry endorsement from Alberta Canola — The organizational body representing 14,000+ canola farmers across Alberta Growers
Integrated drone-based hyperspectral imaging into Leduc County's crop disease surveillance program — replacing manual field inspection with autonomous aerial diagnostics across large parts of the county's canola acreage. Given that clubroot surveillance is a mandated requirement for all Alberta Agricultural Services Boards we see Leduc County as a key partner that can help us scale our solution across the province to the other 68 ASBs. There are two key factors working in a favor, 1. Under the Agricultural Pests Act, Every County in Alberta has a mandate to surveil for clubroot — this creates a pool of mandated buyers 2. Pixal 3D's has the only drone based platform on the market that can detect clubroot at 10x the speed and 10x the accuracy of traditional ground-based surveys — this creates a natural technology moat
Pixal 3D won a contract with Northwest Invasive Plant Council (NWIPC) to use AI and ML to detect and track the spread of invasive weed species across BC. This will be the first time that AI and ML are used to detect and track the spread of invasive weed species across BC. Giving the province a much more detailed information on the spread of invasive weed species across BC. A succesul demonstration could lead to an expanded scope worth over $500K annually — further aligning with our strategy to capture repeatable revenue streams from government contracts.
Amrize, formerly LaFarge— contracted WSP to condct an enviromental assessment of parts of the quarry. This assessment required the classification of trees across the site to count and distinguish between conniferous and deciduous trees. Pixal 3D was contracted to provide the data capture and and processing capabilities to deliver on this project
Pixal 3D's founding team combines business development, drone operations, and machine learning — supported by one of the strongest agricultural research networks assembled for a Canadian agtech startup.
Founders


Core R&D Team





Scientific Advisory Committee





Most seed decks ask investors to price scientific risk, commercial risk, and team risk at once. Pixal 3D has already retired two: peer-reviewed detection co-developed with AAFC and the University of Guelph, and a national advisory board representing 200+ peer-reviewed publications. $1.58M funds the third — two seasons of paid commercial flights through Alberta Services Boards and the NWIPC, on a capital-efficient path to Series A.
Pixal 3D is building Canada's first agricultural AI diagnostic moat. We're actively in conversation with mission-aligned co-investors to close our seed round.