Website Portfolio Copy — Remote Grain Monitoring, Sensor Networks, Silo Instrumentation & Weighbridge Integration
Project positioning
This project represents the second major deployment phase of Seencode, developed for a large grain-storage complex serving northeastern Iran and located approximately 60 km outside Mashhad.
The operational challenge was fundamentally different from the first Seencode cold-storage deployment.
The objective was not primarily barcode-directed warehouse picking. It was to create a remotely observable storage environment capable of monitoring grain conditions across multiple storage geometries and enabling operators in Mashhad to supervise temperature, humidity, and related storage-risk conditions without remaining physically on site.
The project combined:
- distributed environmental sensing,
- Raspberry Pi / edge-style data collection,
- multiple sensor topologies for different storage structures,
- remote web monitoring,
- temperature and humidity supervision,
- monitoring-assisted ventilation / hot-air / cold-air control,
- digital weighbridge integration,
- shipment traceability,
- and a proposed QR-based downstream traceability layer.
Due to operational and security restrictions, photographs and videos from the actual Fajr facility are not published. Any silo or storage imagery used publicly for this project should be clearly labeled as illustrative.
Classification
- Project Family: Seencode
- Project Type: Industrial Environmental Monitoring / Grain Storage / Traceability
- Deployment: Seencode Phase 2
- Site Context: Major grain-storage complex serving northeastern Iran
- Location Context: Outside Mashhad, approximately 60 km from the city
- Primary Domain: Grain storage / agricultural infrastructure
- Core Objective: Remote environmental monitoring and storage-condition supervision
- Storage Types: Long/open storage halls, conical silos, room-type storage
- Monitoring Variables: Temperature, humidity, spatial sensor position, storage context
- Control Support: Ventilation / hot-air / cold-air operational guidance
- Edge / Field Layer: Distributed sensors and field wiring
- Monitoring Layer: Seencode web application
- Integration: Digital weighbridge
- Traceability Extension: QR-based origin / storage / dispatch information
- Portfolio Relevance: Very High
- Suggested CV Relevance: Tier A/B — Industrial Monitoring, IoT & Systems Architecture
Website Card
Title:
Seencode — Grain Storage Environmental Monitoring & Traceability System
One-line Description:
A remote environmental-monitoring and traceability platform for a major grain-storage complex in northeastern Iran, combining distributed sensor networks, silo-specific instrumentation, web supervision, weighbridge integration, and QR-ready supply-chain traceability.
Categories:
- Industrial IoT
- Grain Storage
- Environmental Monitoring
- Agriculture Technology
- Remote Operations
- Sensor Networks
- Traceability
- Weighbridge Integration
- Industrial Digitalization
- Seencode Platform
Technology / Concept Tags:Raspberry Pi · DHT22 · Temperature · Humidity · Sensor Networks · Web Application · Remote Monitoring · Silo Instrumentation · Weighbridge · QR Traceability · Industrial IoT
Executive Summary
The Fajr grain-storage phase of Seencode addressed a different class of operational risk than the earlier cold-storage implementation.
Grain was stored in large facilities outside Mashhad, where continuous in-person supervision was difficult.
The system therefore focused on one central operational question:
What is happening inside the stored grain right now, and can the responsible team in Mashhad detect a developing environmental problem early enough to intervene?
The project was designed around remote visibility into:
- temperature,
- humidity,
- sensor location,
- storage-unit condition,
- and changes over time.
The deployment had to support several physically different storage structures.
A single fixed sensor layout would not have been sufficient.
Seencode therefore used different sensor topologies according to the geometry of each storage area.
Business Context
The facility served as a major grain-storage complex for northeastern Iran.
Grain could arrive through multiple logistics channels and then remain stored for significant periods.
The operational environment created several challenges:
- the site was far from the city,
- storage areas were large,
- grain volumes could be substantial,
- temperature and humidity could vary spatially,
- physical inspection could be slow,
- environmental deterioration could begin locally before becoming visually obvious,
- management needed remote visibility.
The project was therefore designed as an industrial monitoring layer over a physically distributed storage environment.
Core Problem
The main problem was not simply:
“What is the temperature of the building?”
A single temperature reading is not enough for large-scale grain storage.
The meaningful questions are closer to:
- Which storage unit?
- Which position?
- Which depth?
- Which height?
- What temperature?
- What humidity?
- At what time?
- Is the trend moving toward a risk condition?
This creates a multidimensional monitoring problem.
Conceptual Data Model
Measurement
├── Facility
├── Storage Type
├── Storage Unit
├── Sensor
├── Position
├── Depth / Height
├── Temperature
├── Humidity
└── Timestamp
This is significantly stronger than a single room sensor.
High-Level Architecture
Grain Storage Complex
│
┌─────────────────┼─────────────────┐
│ │ │
▼ ▼ ▼
Long/Open Halls Conical Silos Room Storage
│ │ │
Sensor Arrays Pendulum Sensors Fixed Sensors
│ │ │
└─────────────────┼─────────────────┘
↓
Field / Edge Layer
↓
Network Transport
↓
Seencode
Web Application
↓
Remote Monitoring UI
↓
Operational Decisions
↓
Ventilation / Hot / Cold Air
Storage Geometry as an Engineering Constraint
A major design requirement was that each storage geometry needed its own sensing strategy.
The project did not assume:
one sensor topology fits every building.
Instead:
Storage Geometry
↓
Sensor Deployment Strategy
↓
Measurement Quality
This is one of the strongest engineering aspects of the project.
Storage Type 1 — Long / Open Storage Halls
Some long storage halls had approximately 130 meters on each side.
The sensing design had to account for:
- long horizontal distance,
- large stored volume,
- significant vertical height,
- uneven local conditions.
The project therefore proposed cable-based sensing along the structure.
Sensor Coverage Strategy
The approximate sensing coverage radius was considered around 8 meters.
However, relying on the maximum theoretical spacing would create gaps and reduce confidence.
A tighter spacing of approximately 5 meters was proposed to create overlapping sensor coverage.
Conceptually:
Sensor A coverage
\____/
Sensor B coverage
\____/
Overlapping zones reduce blind spots.
This is a practical redundancy strategy.
Why Overlap Matters
Environmental monitoring in stored grain should not depend on one sensor at the edge of its usable range.
Overlapping zones improve:
- continuity,
- fault tolerance,
- local comparison,
- anomaly detection.
If one sensor disagrees sharply with neighboring sensors, that can also become a diagnostic signal.
Vertical Monitoring Challenge
The grain level could rise to approximately 15 meters.
A fixed sensor at floor or ceiling level would therefore provide incomplete information.
The sensing system needed to move vertically through or alongside the stored grain mass.
Steel Cable + Winch Mechanism
The proposed solution used sensors mounted along steel cables.
The cables could be raised or lowered using winches installed at the sides of the storage area.
Conceptually:
Winch
│
Steel Cable
│
Sensor
│
Sensor
│
Sensor
│
Sensor
↓
Variable Depth / Grain Height
This made the sensing topology mechanically adjustable.
Why the Winch System Was Useful
The winch mechanism allowed the sensor assembly to adapt to:
- changing grain height,
- maintenance requirements,
- inspection,
- repositioning,
- access without permanent entry into the grain mass.
This is a hybrid mechanical / digital design problem.
Storage Type 2 — Conical Silos
Conical silos required a different strategy.
The top of the silo provided a natural access point.
The project used a pendulum-like sensor arrangement suspended from the upper nozzle / access point.
Pendulum Sensor Architecture
Top Access / Nozzle
│
┌───────┼───────┐
│ │ │
Cable Cable Cable
│ │ │
Sensor Sensor Sensor
│ │ │
▼ ▼ ▼
Grain Volume
The sensor assemblies were weighted.
Their own weight helped them move deeper into the grain volume as the storage structure was filled.
Why This Was Different
A wall-mounted sensor would only measure conditions near the silo boundary.
The pendulum approach brought sensing closer to the internal grain mass.
That improves the ability to observe local thermal or humidity variation.
Storage Type 3 — Room-Type Storage
Room-style storage areas presented the simplest sensing problem.
The geometry was more accessible and less mechanically constrained.
Fixed sensor placement could therefore be used more directly.
The design still needed to consider:
- room size,
- air circulation,
- grain position,
- local hot/cold zones.
But it did not require the same cable/winch or pendulum mechanics.
Three Sensor Topologies
The resulting architecture can be summarized as:
Long Hall
→ Adjustable cable sensor arrays
Conical Silo
→ Weighted pendulum sensor arrays
Room Storage
→ Fixed distributed sensors
This is a strong example of adapting software/hardware architecture to physical geometry.
Remote Monitoring
The site was approximately 60 km from Mashhad.
The operational objective was to make real-time conditions visible from the city.
The Seencode web application therefore acted as the remote control and monitoring surface.
Web Application Responsibilities
The application could support:
- live sensor values,
- storage-unit selection,
- condition overview,
- filterable views,
- trend analysis,
- alarm conditions,
- historical reports.
The system was designed to reduce the need for constant physical presence at the site.
Monitoring Workflow
Sensor Measurement
↓
Field Device / Edge Layer
↓
Network
↓
Seencode Backend
↓
Database
↓
Web Dashboard
↓
Operator / Manager in Mashhad
Temperature & Humidity Monitoring
The core environmental variables were:
- temperature,
- humidity.
These values are particularly important because poor storage conditions can increase the risk of:
- spoilage,
- moisture damage,
- mold growth,
- quality degradation.
Aflatoxin Positioning — Important Technical Boundary
The project must not claim that temperature/humidity sensors directly detected aflatoxin.
They did not.
The correct technical positioning is:
Seencode monitored environmental conditions associated with grain spoilage and mold/aflatoxin risk, enabling earlier operational intervention when temperature or humidity moved outside defined ranges.
Actual aflatoxin detection requires appropriate laboratory or analytical testing.
This distinction should remain explicit in public documentation.
Monitoring-Assisted Environmental Control
The sensor data was intended to support operational control using:
- ventilation,
- cold air,
- hot air,
- turbine / airflow systems.
The historical project should be described as:
monitoring-assisted environmental control
unless surviving implementation evidence confirms fully automated closed-loop actuation.
Control Loop
Measure
↓
Compare
↓
Identify Risk Condition
↓
Operator / Control Decision
↓
Ventilation / Heating / Cooling
↓
Measure Again
This is an operational feedback loop even if final actuation was manual or semi-manual.
Existing Infrastructure Integration
The project was intended to work with the storage facility rather than replace all existing systems.
This is important in industrial environments.
A practical modernization strategy often means:
Existing Equipment
+
New Sensors
+
New Software Layer
=
Operational Improvement
rather than a complete greenfield replacement.
Digital Weighbridge Integration
A later stage connected Seencode to the digital weighbridge at the facility entrance.
This significantly strengthened traceability.
Inbound Grain Flow
Conceptually:
Incoming Truck / Shipment
↓
Digital Weighbridge
↓
Weight Record
↓
Shipment Identity
↓
Origin / Source
↓
Storage Assignment
↓
Environmental Monitoring
This linked logistics data with storage data.
Shipment Identity
A traceable shipment can be modeled with fields such as:
Shipment
├── Source
├── Supplier
├── Transport Method
├── Arrival Time
├── Gross Weight
├── Tare Weight
├── Net Weight
├── Grain Type
├── Storage Unit
└── Traceability ID
The exact historical field set should only be published where recovered source confirms it.
End-to-End Traceability Vision
The project was moving toward a chain-of-custody model:
Source / Purchase
↓
Transport
↓
Weighbridge
↓
Inbound Record
↓
Storage Unit
↓
Environmental History
↓
Dispatch
↓
QR Code
↓
Receiver / Consumer
This expanded Seencode beyond monitoring.
It became a traceability platform.
QR-Based Downstream Traceability
A later proposed extension generated QR information for outgoing product / shipment records.
The QR layer could expose selected information such as:
- original purchase/source location,
- storage period,
- storage method,
- handling history,
- dispatch information,
- quality/risk status.
Only information approved for public disclosure should be exposed.
Why QR Was Valuable
The QR code made the digital history portable.
Instead of traceability remaining inside the warehouse database, it could travel with the outgoing shipment.
That creates a bridge between:
Internal Operations
↓
External Transparency
Security & Site Confidentiality
The actual Fajr facility was a large strategic grain-storage site.
For operational/security reasons:
Actual site photographs and videos are not published.
Any public images used in the portfolio should be labeled clearly:
Illustrative image — not the actual facility
This protects the project context without weakening the engineering story.
Location Disclosure Policy
The public portfolio does not need to display:
- exact coordinates,
- internal site map,
- sensor placement map,
- infrastructure access points,
- storage-unit identifiers.
A high-level location such as:
Razavi Khorasan / northeastern Iran
is sufficient for recruiter context.
My Role
My role covered the architecture and system-design work across physical and software layers.
Responsibilities included:
- analyzing the grain-storage monitoring problem,
- defining sensing strategies for multiple building types,
- designing long-hall sensor spacing,
- planning overlapping sensor coverage,
- designing the steel-cable / winch deployment concept,
- designing pendulum-based silo sensing,
- defining room-storage sensor placement,
- remote monitoring architecture,
- Seencode web application design,
- environmental data visualization,
- reporting design,
- operational control support,
- digital weighbridge integration concept,
- shipment traceability modeling,
- QR-based downstream traceability design,
- and system-level integration planning.
What Makes This Project Strong
The strongest aspect is that it crosses multiple engineering disciplines.
Mechanical Deployment
+
Sensors
+
Networking
+
Web Software
+
Industrial Monitoring
+
Traceability
+
Logistics Integration
This is not a conventional web project.
It is industrial systems engineering.
Cyber-Physical Architecture
Seencode connected software with physical storage infrastructure.
Physical Grain
↓
Sensors
↓
Edge / Field Layer
↓
Network
↓
Web Platform
↓
Human Decision
↓
Physical Intervention
The system therefore sits between IT and operational technology.
Industrial Digitalization Perspective
The project digitized previously difficult-to-observe storage conditions.
The transformation was:
Manual / Periodic Inspection
↓
Distributed Sensing
↓
Remote Visibility
↓
Condition-Based Decision
This is the essence of industrial digitalization.
Data Quality
A modern implementation should treat sensor quality as a first-class concern.
Potential checks include:
- missing readings,
- unrealistic jumps,
- stale values,
- sensor disagreement,
- communication loss,
- calibration drift.
Sensor Health
A sensor should not be treated as trustworthy simply because it produced a number.
A V2 should maintain:
Sensor
├── Last Reading
├── Last Heartbeat
├── Health State
├── Calibration Date
└── Error Count
Alerting Strategy
A modern alert engine should support:
- high temperature,
- low temperature,
- high humidity,
- rapid temperature change,
- sensor offline,
- storage-unit communication failure.
Alert Hysteresis
Industrial alert systems should avoid repeatedly switching between alarm and normal states around one threshold.
Example:
Alarm ON at 30°C
Alarm OFF at 28°C
This hysteresis reduces alert noise.
Values are illustrative only.
Spatial Analysis
Large grain storage benefits from spatial interpretation.
Instead of one number:
Temperature = 27°C
the system can visualize:
Zone A: 24°C
Zone B: 25°C
Zone C: 31°C
Zone D: 26°C
This reveals localized anomalies.
Heat Map Visualization
A modern Seencode dashboard could generate:
- horizontal heat maps,
- vertical silo profiles,
- depth-temperature diagrams.
This would make hotspots easier to identify.
Time-Series Analysis
Historical analysis can answer:
- Which sensor is warming?
- Is humidity rising over several hours?
- Did ventilation reduce the anomaly?
- Which silo repeatedly develops hotspots?
V2 — Modern Grain Storage Monitoring Platform
Recommended modern project title:
Seencode — Grain Storage Monitoring & Traceability Platform
The V2 should preserve the original multi-geometry sensing concept while modernizing communications, storage, monitoring, and reliability.
V2 Architecture
Sensor Network
↓
Edge Gateways
↓
MQTT / HTTPS
↓
Ingestion Layer
↓
Time-Series Data Platform
↓
Seencode Backend
┌──────────┼──────────┐
▼ ▼ ▼
Monitoring Alerts Traceability
│ │ │
└──────────┼──────────┘
↓
Web Dashboard
V2 Edge Architecture
A modern deployment could use:
- Raspberry Pi gateways,
- industrial controllers,
- RS-485 sensor networks,
- Modbus,
- LoRa / wired sensor nodes,
- MQTT uplink.
Technology should be selected based on distance, reliability, and environmental conditions.
Why Wired Networks Still Matter
Large storage facilities can be electrically and physically challenging environments.
Wired industrial communication may provide advantages in:
- stability,
- predictable latency,
- maintenance,
- electromagnetic resilience,
- reduced dependence on Wi-Fi coverage.
V2 Data Storage
Environmental data is time-series data.
Recommended options:
- PostgreSQL + TimescaleDB,
- or a dedicated time-series database.
Core record:
timestamp
facility_id
storage_unit_id
sensor_id
position
depth
temperature
humidity
quality
V2 Facility Model
Facility
↓
Storage Area
↓
Storage Unit
↓
Sensor Group
↓
Sensor
Each unit can also store geometry metadata.
Geometry-Aware Monitoring
A modern platform should preserve the concept that geometry affects sensor interpretation.
Example:
Storage Type:
LONG_HALL
CONICAL_SILO
ROOM
Each type can have its own:
- sensor layout,
- visualization,
- alert rules,
- maintenance procedure.
Digital Twin Direction
A future Seencode implementation could create a lightweight digital twin of each storage unit.
The model would include:
- dimensions,
- sensor coordinates,
- grain level,
- sensor values,
- airflow status.
This would make visualization significantly more powerful.
Weighbridge V2 Integration
A modern integration could ingest weight records automatically.
Vehicle
↓
Weighbridge
↓
Inbound Event
↓
Shipment Record
↓
Storage Assignment
This reduces manual re-entry.
Lot / Shipment Traceability
A grain lot should be traceable from:
Source
→ Arrival
→ Weight
→ Storage
→ Conditions
→ Movement
→ Dispatch
QR Traceability V2
A QR endpoint could expose a controlled subset of lot history.
Example:
Grain Type
Origin
Arrival Date
Storage Facility
Storage Period
Condition Summary
Dispatch Date
Sensitive infrastructure details should remain private.
Risk Scoring
A modern platform could calculate an operational risk score based on:
- temperature trend,
- humidity trend,
- duration outside target range,
- sensor agreement,
- storage duration.
This is a decision-support score, not a laboratory safety certification.
Aflatoxin Risk Boundary
Any mold/aflatoxin-related score should be labeled as:
environmental risk indicator
not:
aflatoxin detection
unless an actual validated analytical sensor/lab test is integrated.
Roles
Potential V2 roles:
- Administrator
- Operations Manager
- Quality Control
- Facility Operator
- Maintenance Technician
- Auditor
- Read-Only
RBAC
Permissions can include:
- view telemetry,
- configure thresholds,
- acknowledge alerts,
- modify sensor assignments,
- view weighbridge data,
- manage traceability records.
Audit Trail
Important actions should be logged:
- threshold changes,
- sensor reassignment,
- alert acknowledgement,
- storage assignment,
- weighbridge correction,
- QR/public-data configuration.
Offline Operation
Remote facilities may lose external connectivity.
Edge gateways should therefore support:
Measure
↓
Store Locally
↓
Connection Available?
├── Yes → Sync
└── No → Buffer
No telemetry should be lost during short communication outages.
Device Heartbeat
The platform should distinguish:
Normal reading
from:
No reading because sensor/device is dead.
Every gateway and sensor group should have a heartbeat state.
Redundancy
For critical storage zones, overlapping sensors provide redundancy.
A modern system can compare adjacent sensors and flag abnormal disagreement.
Calibration
Sensor calibration should become part of the maintenance model.
Each sensor can store:
- installation date,
- calibration date,
- next calibration,
- calibration offset.
Maintenance Workflow
The web application can generate tasks such as:
- sensor replacement,
- cable inspection,
- winch inspection,
- calibration,
- network repair.
Automated Testing
Domain Tests
- threshold logic,
- risk score,
- sensor status,
- shipment traceability.
Integration Tests
- MQTT ingestion,
- weighbridge data import,
- QR traceability,
- notification delivery.
Reliability Tests
- gateway offline,
- sensor failure,
- duplicate message,
- delayed data,
- reconnect synchronization.
Observability
Platform metrics could include:
- active sensors,
- offline sensors,
- message rate,
- telemetry delay,
- alert count,
- storage units at risk,
- gateway health.
Reporting
Useful reports include:
- temperature history,
- humidity history,
- alarm history,
- sensor uptime,
- storage-unit condition summary,
- incoming shipment history,
- storage duration,
- weighbridge reconciliation.
Security
A production industrial deployment should include:
- device authentication,
- TLS,
- per-device credentials,
- RBAC,
- audit logging,
- encrypted transport,
- network segmentation,
- backup strategy,
- secure remote administration.
Why This Project Matters
The project matters because it did not treat the facility as a generic building.
It treated grain storage as a spatial, physical, time-dependent system.
The core design recognized that:
Different Geometry
↓
Different Sensor Topology
↓
Different Measurement Strategy
That is the architectural insight behind the project.
Engineering Highlights
- Designed a remote monitoring architecture for a major grain-storage complex serving northeastern Iran.
- Developed different sensor deployment strategies for long halls, conical silos, and room-style storage.
- Proposed overlapping sensor coverage to reduce blind zones.
- Designed adjustable steel-cable/winch sensor deployment for tall storage volumes.
- Designed weighted pendulum sensor arrays for conical silos.
- Built the Seencode web-monitoring concept for remote operation from Mashhad.
- Integrated environmental monitoring with operational ventilation/heating/cooling decisions.
- Extended the platform toward digital weighbridge integration.
- Designed a traceability chain from source and inbound weight through storage history and dispatch.
- Proposed QR-based downstream transparency for outgoing grain.
- Maintained a clear technical boundary between environmental-risk monitoring and laboratory aflatoxin detection.
Career Evidence
This project demonstrates:
- Industrial IoT
- Sensor Network Architecture
- Remote Monitoring
- Cyber-Physical Systems
- Agricultural Technology
- Environmental Monitoring
- Mechanical / Digital Integration
- Edge Computing
- Web Platform Architecture
- Time-Series Data
- Industrial Digitalization
- Traceability
- Weighbridge Integration
- Systems Thinking
- Solution Architecture
CV Relevance
Tier A/B — Major Portfolio Project
This project is suitable for senior engineering and architecture discussions because it spans:
Physical Infrastructure
→ Sensors
→ Mechanical Deployment
→ Networking
→ Edge Layer
→ Backend
→ Monitoring
→ Operations
→ Traceability
It is particularly relevant to:
- Solution Architecture
- Industrial IoT
- Smart Agriculture
- Edge Computing
- Monitoring Platforms
- Operational Technology
- Digital Transformation
Interview Story
Situation
A major grain-storage complex serving northeastern Iran was located approximately 60 km outside Mashhad.
Management needed better remote visibility into storage conditions because the facility contained multiple large storage geometries and physical inspection alone could not provide continuous coverage.
Task
The solution needed to monitor temperature and humidity across different storage structures and make the data remotely accessible from Mashhad.
Action
I designed different sensing topologies for three storage types:
- overlapping cable-mounted sensors in long halls,
- weighted pendulum sensor arrays for conical silos,
- fixed sensor layouts for room-type storage.
I designed the Seencode web-monitoring layer, integrated the concept with environmental control decisions, and later extended the architecture toward digital weighbridge integration and QR-based traceability.
Result
The project created a unified monitoring and traceability concept capable of observing large grain-storage environments remotely while adapting the sensing architecture to the physical geometry of each storage type.
Suggested Website Full Description
Overview
Seencode — Grain Storage Environmental Monitoring & Traceability System was the second major deployment phase of the Seencode platform.
It was designed for a large grain-storage complex serving northeastern Iran and located approximately 60 km outside Mashhad.
The objective was to provide remote visibility into environmental conditions inside grain-storage structures with very different geometries.
The Challenge
A single room sensor could not represent the condition of a large grain volume.
Temperature and humidity could vary locally and at different depths.
The storage complex also included different structures:
- long halls,
- conical silos,
- room-style storage.
Each required a different sensing strategy.
Original Solution
For long halls, sensor arrays were designed with overlapping coverage and mounted on steel cables that could be raised or lowered through winches.
For conical silos, weighted pendulum-style sensor arrays were suspended from the upper access point and allowed to penetrate the grain volume.
For room-style storage, fixed sensor placement was sufficient.
The resulting telemetry was intended for remote monitoring through the Seencode web application.
Operational Control
Temperature and humidity data supported operational decisions involving:
- ventilation,
- hot air,
- cold air,
- storage-condition intervention.
The platform monitored environmental conditions associated with spoilage and mold/aflatoxin risk but did not directly detect aflatoxin.
Traceability Expansion
The later architecture connected Seencode with the digital weighbridge at the facility entrance.
This allowed shipment identity, source, weight, storage assignment, environmental history, and dispatch to be linked into one traceability chain.
A further QR-based concept was proposed for downstream access to selected product-history information.
Security & Evidence
Due to security and operational restrictions, actual photographs and videos from the Fajr storage complex are not published.
Any facility imagery shown publicly is illustrative.
Short Website Card Copy
Seencode — Grain Storage Environmental Monitoring & Traceability System
A remote industrial monitoring platform for a major northeastern-Iran grain-storage complex, combining geometry-specific sensor networks, web supervision, weighbridge integration, and QR-ready traceability.
Alternative One-Liners
Architecture-oriented
Designed a geometry-aware sensor and web-monitoring architecture for long grain halls, conical silos, and room-style storage.
IoT-oriented
Built the Seencode grain-storage monitoring concept around distributed temperature/humidity sensing, remote supervision, and industrial environmental control.
Traceability-oriented
Extended grain monitoring into an end-to-end traceability model linking inbound weight, source, storage conditions, and outbound QR records.
Systems-oriented
Combined mechanical sensor deployment, edge monitoring, web software, and logistics data into one industrial grain-storage supervision platform.
Suggested Website Slug
/projects/seencode-grain-storage-monitoring
Alternative:
/iot/seencode-grain-traceability
Relationship to Seencode Phase 1
This project is a standalone portfolio page.
It belongs to the same Seencode family as:
Seencode — Cold Storage & Warehouse Execution System
The two projects should remain separate because they solve different engineering problems.
Phase 1
Warehouse execution, barcode tracking, forklift guidance, FIFO enforcement.
Phase 2
Environmental monitoring, sensor-network design, silo instrumentation, weighbridge integration, grain traceability.
Both can link to each other through a small footer:
Part of the Seencode Industrial Monitoring & Traceability Platform
Cover Direction
Title
Seencode
Subtitle
Grain Storage Environmental Monitoring
Supporting Line
Sensor Networks · Remote Monitoring · Silo Instrumentation · Traceability
Visual Direction
A premium square industrial/agricultural technology cover showing:
- large grain silos,
- long storage hall,
- sensor cables descending into grain,
- pendulum sensor arrays inside a silo,
- temperature/humidity telemetry,
- remote web dashboard,
- digital weighbridge,
- traceability flow.
The cover should communicate:
GRAIN STORAGE
+
SENSORS
+
REMOTE MONITORING
+
INDUSTRIAL CONTROL
+
TRACEABILITY
Do not use actual restricted facility photographs.
Use clearly illustrative imagery.
Evidence Gallery Recommendation
Public / Safe Evidence
- illustrative grain-silo photographs,
- illustrative satellite-scale contextual image if needed,
- architecture diagrams,
- sensor-topology diagrams.
Restricted Evidence
Do not publish:
- actual internal facility videos,
- exact internal layouts,
- detailed security-sensitive infrastructure,
- precise sensor placement maps,
- restricted site photographs.
Software Evidence
If recoverable:
- Seencode monitoring UI,
- alarm configuration,
- storage-unit dashboards,
- weighbridge integration screen,
- traceability records,
- QR prototype.
Final Portfolio Statement
The engineering challenge in this project was not simply collecting temperature and humidity.
It was designing a monitoring system for a physical environment where:
- storage geometry changes the sensing problem,
- grain depth changes the sensing problem,
- distance from the city changes the operational problem,
- and traceability changes the data problem.
Seencode therefore had to connect:
PHYSICAL STORAGE
+
SENSOR TOPOLOGY
+
REMOTE MONITORING
+
OPERATIONAL CONTROL
+
LOGISTICS DATA
+
TRACEABILITY
The result was a platform architecture designed to make a remote grain-storage facility observable, manageable, and traceable from outside the site.
That is the core of the project:
not a sensor dashboard, but an industrial grain-storage monitoring and traceability system.