Crop monitoring
See stress before it spreads
Track canopy change, disease pressure, water stress, and field variability from imagery and field observations.
A Vicub Labs product for autonomous agriculture operations
Give every field a real time AI command center
AgriSentinel AI connects satellite imagery, drone scouting, soil sensors, and weather models to help growers detect risk earlier, plan field work faster, and operate every acre with measurable intelligence.
Live Field Intelligence
Orchard Block C-12
Risk
Crop risk monitoring
Water
Irrigation planning
Tasks
Field work tracking
Autonomous field note
Canopy stress detected in row 8
Recommend drone rescan, leaf tissue sampling, and irrigation adjustment before the next heat window.
Observe
Field signals
Imagery, sensors, weather, notes, and work logs organized around each field.
Analyze
Risk context
AI-assisted interpretation of crop stress, input timing, and operational priority.
Act
Task plans
Recommendations converted into scouting, sampling, irrigation, or treatment work.
Report
Operating record
Field decisions and outcomes captured for managers, advisors, and stakeholders.
Core Pain Points
Farms are becoming data-rich, but decision-poor
Growers already collect weather feeds, sensor readings, camera images, irrigation logs, labor notes, and compliance records. The gap is turning those signals into timely operating decisions.
Core Pain 01
Risk is detected too late
Disease, water stress, and lodging are often visible only after yield loss has already started.
Core Pain 02
Decisions rely on scarce expertise
Agronomist judgment is hard to scale across fields, crops, seasons, and distributed farm teams.
Core Pain 03
Farm data never closes the loop
Equipment, imagery, weather, and work logs stay fragmented instead of becoming executable operating intelligence.
Core Pain 04
Compliance takes operator time
Traceability, input records, audits, and food-safety reporting still require too much manual work.
Solutions
Operational intelligence for the daily decisions farms actually make
AgriSentinel AI is built around the workflows where delayed decisions become expensive: scouting, input timing, irrigation, labor coordination, risk reporting, and season planning.
Irrigation planning
Time water by crop need
Combine weather, soil moisture, and growth stage context to support more deliberate irrigation scheduling.
Field operations
Turn insight into work
Convert risk signals into scouting, sampling, treatment, and follow-up tasks that field teams can execute.
Reporting
Document every decision
Create concise operating summaries for growers, advisors, buyers, insurers, and internal management reviews.
Product Features
The agriculture AI stack from detection to dispatch
✦
Multi-source field sensing
Fuse satellite NDVI, drone imagery, soil moisture, weather forecasts, and historical work logs into a live field profile.
Output: field condition timeline
⌁
Early pest and disease alerts
Detect leaf texture changes, canopy anomalies, and spread patterns, then return risk levels, likely causes, and treatment windows.
Output: crop risk map
◈
Irrigation and nutrition guidance
Combine evapotranspiration, soil conditions, and crop stage to generate strategies for water savings, yield stability, and cost control.
Output: input timing recommendation
▣
Yield forecasting engine
Continuously calibrate forecasts by field, variety, and batch to support procurement, storage, insurance, and sales timing.
Output: yield outlook brief
↯
Farm task orchestration
Turn AI recommendations into scouting, spraying, irrigation, and sampling tasks with owners, equipment, and completion tracking.
Output: task recommendation
◎
Traceable operating reports
Generate risk, cost, yield, and sustainability reports for growers, cooperatives, insurers, and crop buyers.
Output: field report
How It Works
From field signal to operating decision in four steps
01
Connect data sources
Bring together imagery, weather, sensor feeds, field notes, and crop context without forcing operators into a new workflow on day one.
02
Analyze field context
AI models compare field signals with crop stage, weather windows, and prior observations to surface the highest-priority risks.
03
Recommend next action
Each recommendation includes the reason, urgency, field location, suggested owner, and the evidence behind the decision.
04
Track outcome
Completed tasks, photos, notes, and results become an operating record for the next field visit, audit, or season review.
Use Cases
Built for the operators who need field-level clarity
The platform is designed for farms and agriculture partners that already have data, teams, and operating pressure, but need a clearer way to turn signals into decisions.
Greenhouses and controlled environments
Monitor crop uniformity, disease pressure, humidity-sensitive risk, and fertigation adjustments across zones and production cycles.
Orchards and high-value crops
Prioritize scouting routes, canopy stress, water timing, pest pressure, and harvest readiness across large blocks.
Open-field farm operations
Combine weather, imagery, and work logs to support spray windows, irrigation decisions, and field-level risk tracking.
Cooperatives, advisors, and insurers
Create consistent field summaries, risk documentation, and intervention records across many growers and locations.
Data & Integrations
Designed to work with the agriculture data farms already collect
AgriSentinel AI can begin with simple field notes and weather signals, then expand into richer data sources as the pilot grows.
Satellite imagery
NDVI, canopy change, field variability, and season-over-season comparisons.
Drone imagery
Closer inspection of canopy stress, pest patterns, stand gaps, and scouting zones.
Soil sensors
Moisture, temperature, and field-level conditions that influence irrigation timing.
Weather data
Forecast windows, heat stress, precipitation, disease risk, and application timing.
Field notes
Photos, observations, scouting comments, crop stage updates, and advisor input.
Work logs
Irrigation, spraying, sampling, labor, equipment, and follow-up records.
Pilot & Commercial Access
Start with a scoped pilot, then expand by operational value
Engagements are scoped by crop type, acreage, available data sources, operational complexity, and the level of advisory support required.
Pilot Evaluation
For one crop system, farm block, or greenhouse zone
Scoped
- ✓ Field data review and pilot plan
- ✓ Risk monitoring setup
- ✓ Weekly operating summaries
- ✓ Founder-led onboarding
Recommended
Commercial Rollout
For multi-field operators and growing teams
Custom
- ✓ Multi-field monitoring workflows
- ✓ Drone, sensor, and weather integrations
- ✓ Task recommendations and reporting
- ✓ Operating reviews with Vicub Labs
Enterprise Program
For agriculture groups, insurers, and data-rich partners
Partner
- ✓ Multi-organization operating model
- ✓ Private deployment discussion
- ✓ Custom reporting and API planning
- ✓ Governance, retention, and access controls
About Us
Built by Vicub Labs for operators who manage land, crops, and risk
AgriSentinel AI is an autonomous agriculture intelligence product developed by Vicub Labs. The platform is designed for modern farm operators who need earlier risk visibility, clearer field priorities, and stronger evidence behind every agronomy decision.
We focus on high-value crops, scaled farms, cooperatives, and agricultural insurance workflows. Our team combines remote sensing, agronomy, AI product design, and supply chain analytics to turn field data into practical operating decisions.
Privacy Policy
Farm data should belong to the farm, not the platform
We collect only the data required to provide the service, including field boundaries, sensor readings, imagery, work logs, and business information authorized by the customer.
Customers retain ownership of their original farm data. We do not sell, rent, or share identifiable farm operating data with third parties without explicit authorization.
The platform uses encrypted transport, role-based access, audit logs, and data retention controls by default. Enterprise customers may choose private deployment, regional storage, and custom model training boundaries.
During a pilot, contact information and submitted inquiry details are used to respond to business questions, scope pilot programs, and provide product updates related to AgriSentinel AI.
Customers may request access, correction, export, restriction, or deletion of personal information where applicable. Privacy questions can be sent to kr@vicub.com.
FAQ
Questions a real pilot partner would ask first
Why is the company called Vicub while the product is AgriSentinel AI?
Vicub Labs is the company building autonomous intelligence systems. AgriSentinel AI is our agriculture product.
Do we need sensors before starting?
No. A pilot can begin with field boundaries, crop context, weather data, photos, and field notes, then expand into sensors or drone data later.
What happens during a pilot?
We define the crop system, data sources, risk questions, reporting cadence, and the field decisions the pilot should support.
How do we contact the founder?
Use the contact form or email kr@vicub.com for pilots, partnerships, and product questions.
Contact Us
Start an AgriSentinel AI pilot
If you operate a farm, cooperative, agriculture group, or agtech investment team, contact the Vicub Labs team behind AgriSentinel AI for a pilot plan and product briefing.
Location: San Francisco / Singapore / Remote
Response: Within one business day