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VFACE

Harvests, optimised.intelligently

Precision farming with IoT sensors, crop intelligence AI, and E-Nose olfactory sensing — for agricultural enterprises where yield and quality are the bottom line.

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25%
Average Yield Improvement
30%
Reduction in Input Costs
40%
Improvement in Quality Grading Accuracy

The challenge

Agriculture needs more yield from less land, water and input

Climate volatility

Weather swings make timing decisions riskier. Field-level sensing and forecasting reduce guesswork.

Input costs and sustainability targets

Fertiliser, water and energy are under cost and regulatory pressure. Variable-rate application cuts waste.

Supply chain traceability

Buyers want proof of origin and quality, from field to shelf.

What we deliver

Precision Farming & IoT Sensors

Soil moisture, temperature, humidity, and nutrient sensor networks delivering field-level data to farm management dashboards — enabling zone-specific irrigation and fertilisation that cuts input waste by 25–35%.

Crop Intelligence AI

Satellite imagery analysis, yield prediction models, and disease detection algorithms that give agronomists 4–6 week advance warning of crop stress, pest infestation, and harvest timing optimisation.

E-Nose Olfactory Quality Sensing

VFACE proprietary E-Nose electronic olfactory technology for automated crop freshness grading, post-harvest quality assessment, and contamination detection — replacing manual sensory inspection with objective chemical compound profiling.

Supply Chain Traceability

Blockchain-based provenance tracking from field to consumer, supporting certification claims (organic, fair trade, geographic indication) with tamper-proof harvest records that satisfy export market audit requirements.

How we build

Engineering principles behind every engagement

Rugged and low-power

Sensors and gateways are built for dust, rain, and weeks without a charger.

  • LoRaWAN and NB-IoT options
  • Solar-powered nodes
  • Offline buffering

Technology we work with

  • LoRaWAN
  • MQTT
  • Sentinel-2 imagery
  • Google Earth Engine
  • Azure IoT
  • AWS IoT Core
  • TensorFlow
  • Hyperledger Fabric
  • PostGIS
  • Flutter
  • Grafana
  • Node-RED
  • LoRaWAN
  • MQTT
  • Sentinel-2 imagery
  • Google Earth Engine
  • Azure IoT
  • AWS IoT Core
  • TensorFlow
  • Hyperledger Fabric
  • PostGIS
  • Flutter
  • Grafana
  • Node-RED

Your first 90 days

What you can expect, phase by phase

Days 1–30

Survey and select

  • Farm and crop assessment
  • Connectivity survey
  • Priority use case chosen
  • Pilot fields mapped
Days 31–60

Install and learn

  • Sensor and gateway deployment
  • Satellite and weather data fused
  • First irrigation and risk models
  • Mobile app for farm teams
Days 61–90

Act and measure

  • Live recommendations in season
  • Water and input savings tracked
  • Traceability pilot with a buyer
  • Plan for more fields

Client outcome

Agriculture
Palm oil plantation group (42,000 hectares)

A Malaysian palm oil group was experiencing 18% crop rejection rates at export quality checks due to inconsistent manual freshness grading across 12 collection stations — each rejection costing $340 per tonne in regrading, transport, and market price penalties.

43%Reduction in crop rejection rate
$2.4MAnnual grading loss reduction
60%Faster quality assessment vs manual

VFACE deployed E-Nose olfactory sensors across all 12 collection stations, calibrated to the group's specific Fresh Fruit Bunch quality parameters. Chemical compound profiles replaced subjective manual grading — producing consistent, objective quality scores that aligned with export buyer specifications and reduced rejection disputes.

Solution

  • →VFACE E-Nose units deployed at 12 collection stations with automated grading workflow
  • →Calibration against 3,200 manually-graded reference samples specific to the plantation's cultivar
  • →Blockchain-based quality certificate issuance at point of grading for export documentation
  • →IoT soil and canopy sensors across 8,000 ha pilot zone feeding crop yield prediction model
VFACE E-Nose Platform
Hyperledger Fabric
IBM Cloud IoT
Python / scikit-learn
LoRaWAN

Industry benchmark

Agriculture programmes: VFACE vs typical

Water use reduction

VFACE programmes30%
Industry average12%

Yield improvement

VFACE programmes18%
Industry average8%

Time to trace a batch to origin

VFACE programmes2 hrs
Industry average72 hrs

Lower is better

Figures are indicative ranges from typical engagements compared with published industry surveys. Results vary by scope and starting point.

Client voices

What our clients say

30%

less water used

“We cut irrigation water by almost a third in the first season, and the app works fine with the signal we have.”
B. NakamuraOperations Manager, Commercial horticulture group

Security and governance

Data ownership, sustainability and food safety

Farmer data ownership

Growers own their data and control who sees it.

Food safety standards

Records support GlobalG.A.P. and HACCP-style audits.

Sustainability reporting

Water, energy and input metrics ready for ESG disclosure.

Resilient connectivity

Designed to keep working through patchy rural networks.

Insights and news

What is happening in the industry, and what we think

Industry headlines, last 30 days

Headlines via Google News. Third-party content; VFACE does not endorse the linked articles.

From the VFACE blog

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Frequently Asked Questions

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