CASE STUDY / GREENPHYTO, SINGAPORE
An AI operating architecture for commercial farming.
Inside Greenphyto’s AI-powered Farm Operations Management System: connecting computer vision, IoT, robotics, automation and ERP to make farm operations more predictive and less dependent on routine manual checks.
Measured workflow improvements
INSPECTION
90% less time
Crop inspection reduced from 30 minutes to 3 minutes per growing cage.
Per-cage inspection workflow
CONSISTENCY
70% → 95%
Reported improvement in crop uniformity / yield consistency for Kailan.
+25 percentage points
LIGHTING
60–90× faster
Light intensity adjustment reduced from 2–3 minutes to 2 seconds per cage.
Workflow timing
WORKFORCE
2–3 FTEs
Routine inspection effort freed for quality assurance and process optimisation.
Labour redeployment
From manual checks to proactive decisions.
The challenge
High-density farming depends on precise lighting, climate, nutrients, cage movement and harvest timing. Manual observation and retrospective reporting made defects harder to catch early and harvest output harder to forecast.
Late crop-risk detection
Limited objective growth measurements
Manual follow-up across disconnected systems
The response
FOMS™ combines crop images, environmental signals and production events into one operations layer. Teams use measured phenotypes, yield forecasts and exception alerts to decide what requires attention.
Continuous monitoring
Predictive visibility
Human expertise for validation and action
ONE CONNECTED DIGITAL THREAD
AI embedded in the operation.
ERP connects the production plan. FOMS™ coordinates workflows. Cameras and IoT create visibility. Robotics and automation connect decisions to physical processes.
The challenge
ERP, batch traceability and production workflows.
Sense & analyse
Computer vision, IoT signals and predictive models.
Act & optimise
AMRs, equipment integration, lighting and exception management.
AI across the production journey.
01 / PLANNING
ERP & batch traceability
Connect production, finance, procurement, inventory and fulfilment. Trace a batch through operational and compliance workflows.
Traceability: 5–10 min → 1–2 min
02 / SEEDING & GROWING
Board and tray identification
Camera-based identification of boards, trays and other items supports inventory actions at seeding, transplanting and transfer points.
Computer vision + workflow data
03 / MATERIAL HANDLING
Lift, lifter & pallet workflows
VT lift integration, tray retrieval, pallet counting and AMR transport combine cameras, connected equipment and autonomous movement.
AI + IoT + robotics
04 / MONITORING
Crop growth & health
Track plant growth and detect risks such as yellowing, tip burn and bolting. Surface image-capture gaps and operational exceptions.
Phenotyping + early intervention
05 / HARVEST PLANNING
Yield prediction
Kailan models report correlation above 86%, with an average prediction error of ±6.47 g per plant. Forecasts inform harvest and labour planning.
Crop-specific model results
06 / OPERATIONS
Lighting & decision support
Automated lighting checks flag anomalies. Shared dashboards, alerts and chatbot-assisted retrieval help teams find and act on relevant information.
FOMS™ + IoT + dashboards
People at the centre of an operator-light farm.
Apply proven capabilities to the workflows and priorities of your industry.
Train through real operations
On-floor demonstrations, dashboard walkthroughs, SOP updates, mobile access and user feedback embed AI into daily work.
Move to exception-based management
Production, engineering and digital teams review alerts and forecasts together. Staff focus on quality assurance, predictive decisions and continuous improvement.
Measured workflow improvements
The application identifies future initiatives in robotic harvesting, autonomous monitoring, vision-based pest detection, enhanced yield forecasting, AI-driven digital twins and energy optimisation.
See connected operations in a working farm.
Explore how Arber can translate Greenphyto’s implementation experience into the needs of your operation.