Chapter 01
Digitization
Replace subjective manual monitoring with measurable sensor data.
Replace instinct-only checks with a view of the living leaf bed.
Observe01
INDUSTRIAL IoT · TEA PROCESS INTELLIGENCE
SPECTRA
LEAF
Digitizing the Fermentation Sweet Spot
Spectra Leaf aims to make black-tea oxidation measurable and repeatable by capturing temperature, gas and colour telemetry for live factory guidance and future quality-control models.
3Sensor DomainsTemperature · Gas · Colour
Real-TimeBatch TelemetrySynchronized sensing
ServerlessAWS ArchitectureElastic cloud services
24/7Remote MonitoringConnected visibility
AI-ReadyStructured DatasetBatch-linked profiles
01Project overview
Spectra Leaf aims to digitize the most judgement-sensitive stage of black-tea production and help factories reproduce a better fermentation sweet spot from measured evidence.
Spectra Leaf is an Industrial IoT system that measures what happens across the tea bed, sends the signals to a secure cloud platform and turns them into live operational guidance.
Tea makers commonly call the post-rolling stage “fermentation”. Technically, the main transformation is enzymatic oxidation: rolling or CTC ruptures the leaf cells, oxygen reaches the compounds inside, and colour and aroma develop across the moist solid leaf bed.
This is the phase Spectra Leaf addresses. Temperature, volatile-gas response and colour are captured together so a factory officer can follow the batch without depending on isolated visual checks alone.
BLACK TEA PROCESS / TARGET PHASE
The project observes the window between rolling and drying.
Leaves are spread as a solid bed while oxygen-driven reactions develop the expected coppery colour and characteristic aroma. Drying then arrests the reaction. Spectra Leaf concentrates its measurements and guidance inside this changing window.
01WitherReduce leaf moisture
02Roll / CTCDisrupt leaf cells
03OxidizeMonitor this phase
04DryStop oxidation
THE AUTOMATED METHOD
Sense. Synchronize. Guide. Learn.
The system automates data capture, cloud synchronization, batch history and live visualization. Expert quality evaluation remains part of the loop and supplies the labels needed for future prediction.
01
Sense
Sample temperature, gas and leaf colour at the same time.
02
Synchronize
Publish batch-linked readings securely through AWS IoT.
03
Guide
Show live progress and device state to the factory officer.
04
Learn
Pair each completed profile with its final Good Leaf Percentage.
Current constraintMANUAL
01Subjective visual judgement
02Inconsistent batch quality
03No synchronized sensor history
04Limited remote visibility
05No suitable local training dataset
Spectra Leaf responseCONNECTED
01Quantified sensor measurements
02Real-time factory guidance
03Batch-linked historical records
04Secure remote access
05AI-ready structured telemetry
ASSEMBLY FILMScroll-driven build sequence
From components to one complete device.
Scroll through the three assembly stages, from the first sensor connection to the finished enclosure.
- 01SENSOR ASSEMBLYBuild the sensing layer.
Mount the temperature, humidity, gas and vision sensors that observe the leaf bed.
- 02PUMP + PCBIntegrate flow control and the custom PCB.
Connect the pump system, relay interfaces and the project-specific controller board.
- 03FINAL DEVICEComplete the full device assembly.
Bring sensing, flow control, enclosure and edge connectivity together as one operational unit.
02Objectives
Journey / 01—04
From a human reading
to a system that learns.
Four deliberate moves connect the factory floor to future intelligence—without removing expert judgement from the process.
Chapter 02
Real-Time Guidance
Give factory officers a responsive dashboard for monitoring active fermentation batches.
Give the shift team immediate context while the batch is active.
Measure02
Chapter 03
Data Harvesting
Build structured fermentation profiles mapped to final Good Leaf Percentage values.
Keep every signal attached to its batch and final quality result.
Understand03
Chapter 04
Future Automation
Prepare for machine-learning-based detection of the optimum fermentation endpoint.
Turn accumulated evidence into future endpoint intelligence.
Automate04
Human observationAssisted intelligence
03Key features
The current prototype combines practical sensing, reliable telemetry and usable factory insight while preparing every batch for future intelligence.
01
SENSOR CHANNELS
Six synchronized inputs
Temperature, humidity, vision and three gas-response channels capture one batch-linked view of the changing tea bed.
02
SHARED PROCESS STATE
Shared live batch control
Starting or stopping fermentation updates the backend-owned process state seen by connected web and mobile clients.
03
EDGE RECOVERY
Resilient camera edge
The ESP32-CAM coordinates sensing and visual context, reconnects after interruptions and resumes secure publishing.
04
SECURE TELEMETRY
Serverless cloud pipeline
AWS IoT Core, Lambda and DynamoDB move telemetry securely without fixed server infrastructure.
05
USER ROLES
Role-aware access
Cognito protects operational data while officers and managers receive interfaces matched to their responsibilities.
06
QUALITY LABEL LINK
Quality-ready evidence
Completed sensor profiles can be paired with expert Good Leaf Percentage results for future model development.
04Architecture
Signal journey / 01—03
One batch identity.
Every handoff intact.
Follow a reading from the warm leaf bed, through a secure cloud state, to the people making the next decision.
01 / EdgePhysical world → telemetry
Capture at the tea bed
The ESP32-CAM aligns every reading with the active device, batch and timestamp before secure transmission.
TemperatureHumidityVisionMQ137TGS2620TGS822
02 / CloudTelemetry → trusted state
Synchronize and preserve
AWS services validate incoming data, retain batch history and maintain one shared fermentation state.
IoT CoreDevice ShadowLambdaAPI GatewayDynamoDB
03 / InterfaceTrusted state → action
Guide the factory team
Authenticated web and mobile views translate the same backend state into role-appropriate controls and insight.
Officer mobileFactory dashboardCognitoLive state
Continuity register05 connected handoffs
01Sensor inputSix physical channels
Temperature, humidity, imaging and three gas-response signals.
02Edge controllerESP32-CAM
Synchronized capture, device identity and secure Wi-Fi publishing.
03Cloud stateIoT Core + Device Shadow
Telemetry transport and one shared RUNNING or STOPPED state.
04Application dataLambda + API Gateway + DynamoDB
Validation, protected operations and batch history.
05Authorized clientsCognito + web + mobile
Role-aware access to controls, trends and completed records.
05Hardware system
The device combines temperature, humidity, imaging and three complementary gas-response sensors around a central ESP32 controller.
MAIN CONTROLLER / ESP32MAIN CONTROLLER
ESP32 controller
The ESP32 is the device control hub. It reads the sensor channels, coordinates pump and relay actions, manages the local display and publishes batch-linked telemetry over secure Wi-Fi.
HARDWARE COMPONENTS
One synchronized sensor array, six distinct signals.
01 / 06
TEAM-DESIGNED HARDWARE
Custom control PCB
The Spectra Leaf V1.0 board consolidates ESP32-CAM control, temperature and humidity inputs, three conditioned gas channels, relay interfaces and regulated power on one project-specific PCB.
- Labelled connections for repeatable sensor assembly
- Integrated controller, display and relay interfaces
- A compact base for field trials and enclosure revisions
FIRMWARE SEQUENCE
A loop designed to recover, report and continue.
01
Connect securely to Wi-Fi
02
Connect to AWS IoT Core
03
Subscribe to Device Shadow
04
Read RUNNING / STOPPED state
05
Sample temperature and humidity
06
Read MQ137, TGS2620 and TGS822
07
Capture an ESP32-CAM frame when required
08
Attach device, batch and timestamp
09
Publish telemetry using MQTT
10
Update reported Shadow state
11
Reconnect after interruption
12
Continue the sensing loop
06Cloud infrastructure
The architecture separates device messaging, shared process state, validation, storage, identity and presentation so each part can scale and recover independently.
CLOUD ARCHITECTURE / DATA + CONTROL
Telemetry moves left to right. Start and stop commands return through the same authenticated path and are synchronized with the edge node through Device Shadow.
01Factory edgeESP32 samples and identifies each batch
02IoT Core + ShadowMQTT telemetry and shared RUNNING state
03Lambda + APIValidate, transform and expose operations
04DynamoDBPersist time-series and batch quality
05Web + mobileRole-specific views through Cognito
CONTROL RETURNDashboard → API → Device Shadow → ESP32
AWS IoT Core
Secure MQTT gateway and device communication layer.
Device Shadow
Synchronizes desired dashboard state with the ESP32 reported state.
AWS Lambda
Runs validation, processing and control logic without persistent servers.
API Gateway
Provides controlled REST endpoints for dashboard operations.
DynamoDB
Stores batch records and timestamped sensor telemetry.
Amazon Cognito
Authenticates users and issues tokens for role-aware access.
AWS Amplify
Connects the Next.js client to identity and cloud services.
07Software platform
The software stack turns synchronized telemetry into clear, role-appropriate actions while keeping identity and protected cloud access in one managed flow.
IMPLEMENTED TECHNOLOGY STACK
Frameworks, languages, cloud services and security components used across the platform.
ExperienceNext.js · React · TypeScript · Tailwind CSS
IntegrationAWS Amplify v6 · REST APIs · MQTT state
Cloud dataDynamoDB · Lambda · API Gateway · IoT Core
IdentityAmazon Cognito · JWT · role claims
CLIENT STACK / AWS AMPLIFY v6
Secure access without operational friction.
Next.js and React power the responsive interface, while TypeScript and Tailwind CSS provide a consistent implementation and visual language. Amplify connects Cognito identity to protected cloud services and preserves session continuity through token refresh.
Active batches, device connectivity and fermentation progress remain visible across desktop, tablet and mobile.
Next.js + React
Responsive operational interface
Amazon Cognito
Managed user identity and sign-in
Protected requests
JWT attached to authorized APIs
Session continuity
Token refresh across active work
Responsive access
Desktop, tablet and mobile
Clear batch state
Active work stays visually prominent
SECURITY / AMAZON COGNITO
Identity is checked before operational data is exposed.
Cognito manages sign-in and session tokens. The client attaches a valid JSON Web Token to protected requests, where authorization rules can distinguish factory officers, factory managers and general managers.
1. Sign inCognito user pool2. VerifyJWT + role claims3. AuthorizeProtected API access
08Data and AI
Every batch creates a synchronized record that connects physical change, cloud data and expert-evaluated final quality.
Open datasets correlating detailed tea-oxidation sensor readings with final factory quality measurements are limited.
Spectra Leaf captures synchronized temperature, gas and colour readings, then links the completed profile to an expert-evaluated Good Leaf Percentage.
DATABASE → AI MODEL → FACTORY GUIDANCE
The prediction layer is the planned outcome of the dataset now being collected; it is not presented as a production model.
01DynamoDBRaw readings + batch records
02Labelled datasetSensor profile + final GLP
03ML trainingFeature engineering + validation
04Operational guidanceFuture stage estimate + alert
01
Capture
The officer starts a batch and the ESP32 samples temperature, gas response and colour together.
02
Transmit
Readings are identified by device, batch and timestamp, then published securely through MQTT.
03
Validate + store
Cloud functions check the payload and persist the time-series profile in DynamoDB.
04
Add quality label
After grading, the expert-evaluated Good Leaf Percentage is linked to the completed batch.
05
Curate the dataset
Profiles are cleaned, aligned and organized into trustworthy model-ready examples.
06
Train + guide
Future models learn process-to-quality patterns and can return stage estimates or alerts.
09Future ML concept
Machine learning is planned future work. The current contribution is a reliable, batch-linked and expert-labelled dataset.
DATASET LAYER ACTIVEINFERENCE LAYER · FUTURE WORK
01 / SIGNAL FUSIONCONCEPT VISUAL
Temporal modelMulti-sensor sequence classification
Batch-linked evidence available now
PROPOSED MODEL FEATURES
Rate of temperature riseVOC trend slopeColour change rateElapsed fermentation timeCombined sensor signaturesHistorical batch similarityGood Leaf Percentage labels
01CaptureSensor profiles by batchACTIVE
02LabelPair profiles with expert GLPDATA PHASE
03TrainValidate a time-series modelFUTURE
10Role-based platform
Three authenticated roles share one data platform while receiving the controls, detail and reporting appropriate to their responsibilities.
01
Live operationsFactory Officer
- Start and stop fermentation
- Assign batch and device IDs
- Follow live sensors and trends
- Review batch history and enter GLP
02
Factory performanceFactory Manager
- Review priced and pending batches
- Manage batch pricing
- Track factory revenue
- Compare batch-level analytics
03
Multi-factory viewGeneral Manager
- See consolidated factory status
- Compare revenue contribution
- Identify top batches and factories
- Review organization-wide charts
ROLE 01 · Live operations
Factory Officer dashboard
A live workspace for starting batches, following every sensor and recording the final GLP result.
ROLE 02 · Factory performance
Factory Manager dashboard
A management view for batch pricing, pending work, revenue tracking and factory-level analysis.
ROLE 03 · Multi-factory view
General Manager dashboard
An executive view that brings factory status, consolidated revenue and organization-wide comparisons together.
MOBILE COMPANION
Live batch context travels with the factory officer.
The mobile interface keeps active batches, latest readings and sensor trends visible away from the desktop command centre.
11Testing + CI/CD
Statuses are editable project markers, not claimed pass rates. The test strategy spans delivery, security, cloud parity and edge resilience.
TEST SUITE / 01Implemented
CI/CD
The following work areas define the validation scope for this layer.
GitHub ActionsEditable status
Automatic checks on pushEditable status
AWS Amplify hosting pathEditable status
Main branch deploymentEditable status
12During the project
A look behind the process: preparing samples, building the sensing system and validating it through hands-on laboratory work.
13Future roadmap
Every card below is a future objective, not a current deployment claim.
FUTURE OBJECTIVES
Larger factory pilot
SYSTEM SCALESensor calibration refinement
SYSTEM SCALEMulti-trough deployment
SYSTEM SCALEAdvanced analytics
INTELLIGENCEMachine-learning model training
INTELLIGENCEAutomated sweet-spot alerts
INTELLIGENCEPredictive batch comparison
DEPLOYMENTCommercial deployment
DEPLOYMENT14Team and supervision
The student team develops the connected system with academic supervision and Crop Science guidance grounded in tea processing and quality evaluation.
01
Nadeera Kothalawala
Role pending team confirmation
Member of the multidisciplinary student team developing the Spectra Leaf fermentation system.
02
Lahiru Dinushan
Role pending team confirmation
Member of the multidisciplinary student team developing the Spectra Leaf fermentation system.
03
Rangana Madhushanka
Role pending team confirmation
Member of the multidisciplinary student team developing the Spectra Leaf fermentation system.
04
Deshan Dinidu
Role pending team confirmation
Member of the multidisciplinary student team developing the Spectra Leaf fermentation system.
Academic and domain guidance
Academic supervision
Crop Science and Computer Engineering supervision connect tea-process knowledge with sensing, software, data validation and delivery.
Academic SupervisorS01
Department of Computer Engineering
Ms. Yasodha Vimukthi
Project Supervisor · Lecturer (Probationary)
Provides project supervision across software architecture, machine learning, data validation and platform delivery.
Academic SupervisorS02
Department of Computer Engineering
Dr. Isuru Nawinne
Project Supervisor · Senior Lecturer
Provides systems-engineering guidance across embedded systems, industrial automation, technical validation and project delivery.
Domain supervisionD01
Department of Crop Science · Faculty of Agriculture
Prof. H.M.G.S.B. Hitinayake
Professor · Crop Science Advisor
Advises the project on tea-plantation context, crop-science considerations and the interpretation of process observations alongside factory quality evaluation.
CLOSING SIGNALMeasure the process.
Measure the process.
Understand the profile.
Perfect the leaf.
Spectra Leaf bridges traditional tea-manufacturing expertise with modern digital infrastructure. The current platform improves fermentation visibility through synchronized sensor telemetry and remote factory guidance. Every monitored batch also contributes to a structured quality dataset, the foundation for future automated detection of the fermentation sweet spot.
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