Smarter systems for reliable and sustainable food production
Biocenza replaces the daily guesswork of greenhouse farming with real time camera vision, AI crop detection, and a roadmap to precision robotic harvesting, starting with Seolhyang strawberry farms in Nonsan and Daejeon, South Korea.
- Built by
- Team Hydroxycitronellal
- Harvest loss addressed
- 12–39% of yield
- Raising
- $30K–50K pilot round
Detected
14
Ripe now
6
Oxygen index
88
A season of farm visits, and one honest guess too many.
Biocenza started with a season of farm visits, not a business plan. We spent it walking greenhouse rows around Nonsan and Daejeon, asking growers how they actually run their operations, and what we heard again and again was guessing.
The more we looked at how much of that lands on ordinary greenhouse families, the more it bothered us. So we built Biocenza.
Founding team
- B
Bhuiyan Hashibuzzaman
Team Leader, AI and Big Data
- S
Saida Zheenbekova
Co Leader, AI and Big Data
- K
Kim Kwang Kyun
Team Member
Every greenhouse decision made by feel is a decision no one can verify.
Mr. Park grows Seolhyang strawberries near Nonsan. Every morning he walks the rows and decides by look and feel whether to fertilize, treat, or pick. Nobody tells him whether he is right.
Harvest timing is a guess
Pick early and the fruit is worth less. Pick late and it softens on the vine. Studies of commercial strawberry farms put that loss at 12 to 39 percent of everything grown.
12–39% of yield lost to harvest timing
Detection is slow and late
One pair of eyes, one row at a time. A gray mold outbreak can spread for days before a farmer walking the rows even notices it.
Chemicals are used without data
Fertilizer goes on heavily because that feels safer than going on late. It is a recurring cost with no data behind it, and those fertilizers release nitrous oxide, now the leading ozone depleting substance in the atmosphere.
Not enough land for the food we need
Food demand climbs while greenhouse land does not. Doing more by hand has reached its limit.
Camera Vision, end to end
Biocenza launches as an AI scanning and monitoring platform, with robotic harvesting following as the next phase. Scanning first means a farmer can adopt Biocenza without buying heavy equipment.
Camera
Standard off the shelf cameras mounted along the growing rows.
Camera Vision
Continuous image capture across the greenhouse, feeding frames into the detection pipeline.
AI Detection
On device models identify crop, ripeness stage, and early signs of disease.
Biocenza Platform
Detections are aggregated per greenhouse alongside environmental readings.
Farmer Dashboard
Results reach the farmer as a clear read: what is ripening, what needs attention.
From ripeness to harvest readiness, without the daily walk through
Every row is scanned continuously. The platform turns raw frames into a plain answer: what is ripening, what needs attention, and when to pick.
2–3 day
Target precision harvest window
Per plant
Oxygen exchange tracking
Real time crop scanning
Replaces the daily manual walk through with continuous camera coverage of every row.
Early pre harvest detection
Tells a farmer what is ripening and when, ahead of the harvest window rather than at it.
Oxygen exchange monitoring
Tracks each plant's oxygen exchange for ongoing eco monitoring of crop and greenhouse health.
Disease and stress detection
Surfaces early visual signs of disease so a farmer can treat a few plants instead of losing a row.
A circular resource loop, not three separate businesses
Every version connects into one resource loop. The result: less waste leaving the farm, lower energy and fertilizer costs, and one integrated system instead of three separate waste streams.
No net new CO2
Internal processes are designed to recycle or offset what the system produces.
Ozone protection
Nitrous oxide from nitrogen fertilizer is confirmed as the leading ozone depleting substance in the atmosphere.
Nitrification inhibitor fertilizer
A research backed formulation that cuts nitrous oxide emissions by roughly 30 percent while raising yield.
The brain stays on the server. The machines stay cheap.
Every Biocenza microbot is a body without a brain: motors, a camera, a gripper and a radio. Detection, path planning and task scheduling all run on the same local server that powers the scanning platform.
Farm server
Detection, planning, task queue
Local link
Private wireless, no cloud hop
Microbot
Motors, camera, gripper, radio
Lifter
Moves harvested crates between growing rows and the packing area.
Up to 15 kg per trip
Porter
Carries tools, trays and input supplies along the row.
Up to 8 kg per trip
Scout
Drives the row with a camera when a fixed camera cannot see far enough.
Camera and sensor mast only
Harvest arm
The six axis picking unit on a conveyor, running server side detection.
Single fruit picking
Robotic harvesting arm
A conveyor belt and AI guided robotic arm that harvest within a two to three day precision window, running on the same detection models that power the scanning platform. Two working prototype units are being built now.
One platform, three versions, each built on the last
Year 1
Crops
Launch the AI scanning and monitoring platform in Korean strawberry greenhouses, and build the robotic harvesting prototype alongside it.
Year 2
Dairy
The same monitoring approach applied to cow health, with automated vaccine tracking.
Year 3
Fish
Aquaculture, starting with olive flounder. This is also where the circular loop closes.
Year 4 and beyond
Global expansion
International expansion into Japan, India, and China, alongside continued expansion into meat.
Korea's smart farming policy is already pointed at this shift
- Korea's Smart Farm Promotion Act set the regulatory framework and financial incentives for smart farm adoption.
- A national plan targets smart agricultural technology across roughly a third of the country's greenhouse area.
- Backed by an estimated $1.5 billion in government funding.
Who we sell to first
Small and mid sized greenhouse strawberry farms in the Nonsan and Daejeon region, family run operations growing Seolhyang, the variety on roughly 80 percent of Korean strawberry farms.
$4.8B
Global harvesting robot market projected to surpass $4.8 billion by 2032.
AI and Big Data engineering, with the local knowledge a Korean market needs
Bhuiyan Hashibuzzaman
Team Leader, AI and Big Data
Saida Zheenbekova
Co Leader, AI and Big Data
Kim Kwang Kyun
Team Member
The team has built and demonstrated an AI powered humanoid robot together. It is not the harvesting arm and it is not the product, but it is the reason the team is confident about building one.
Investment vision
We are raising $30K–50K to put Version 1 in front of the farmers who need it first.
A deliberately small round, sized to prove the system on real Korean farms rather than to scale before it is earned. Public funding routes, including Korea's TIPS program, open up after pilot data.
Read the investment casePrototype fleet
Building the two costed out harvesting units, then hardening them for a working greenhouse.
Pilot deployments
Installing the scanning platform in a small number of working Korean greenhouses.
Platform development
Taking the scanning software from prototype to something farms can rely on through a full season.
Intellectual property
Patent filing and a freedom to operate review, before commercial deployment rather than after.
A farmer's relief and a planet's benefit, starting with the same decision.
Whether you grow strawberries near Nonsan, represent a cooperative, or want to back the pilot round, we would like to talk.