Requirements Specification Document AgTech Platform for Small/Medium Farms - Algeria Document Version: 1.0 Date: October 2025 Classification: Confidential
- Executive Summary 1.1 Project Overview Development of an integrated agricultural technology platform leveraging satellite imagery, AI-powered advisory services, and marketplace functionality to empower small and medium-scale farmers (5-50 hectares) in Algeria. The platform addresses critical inefficiencies in agricultural production, contributing to national food security objectives while reducing the country's $10+ billion annual food import bill. 1.2 Key Business Objectives
Increase farm yields by 20-30% through precision agriculture Reduce input costs by 15% via optimized resource usage Improve farmer income by 25% through direct market access Contribute to 10% reduction in national food imports within 3 years Achieve 10,000 active farmers on platform within 18 months
1.3 Success Metrics
Average yield improvement per hectare Reduction in pesticide/fertilizer usage Average price premium achieved vs. traditional middlemen Time reduction in selling harvest (from weeks to days) Equipment utilization rate improvement from 40% to 70%
- Stakeholder Analysis 2.1 Primary Stakeholders Small/Medium Farmers
Profile: 5-50 hectare holdings, mixed crops, family operations Age Range: 35-60 years (younger generation often migrated to cities) Tech Literacy: WhatsApp proficient, limited smartphone usage Languages: Darija (primary), Berber (regional), French (secondary) Pain Points:
No access to agronomists (cost prohibitive) Price exploitation by middlemen (30-40% margins taken) Inability to predict/prevent crop diseases Expensive equipment sitting idle most of year No market price transparency
Agricultural Buyers
Types: Restaurants, supermarkets, food processors, exporters Requirements: Consistent supply, quality assurance, traceability Pain Points: Unreliable suppliers, quality variations, no direct farmer access
2.2 Secondary Stakeholders
Agricultural cooperatives (UNPA, CNMA) Ministry of Agriculture (MADR) Agricultural input suppliers (seeds, fertilizers, pesticides) Equipment owners (tractors, harvesters) Agricultural banks (BADR, CNMA)
- Functional Requirements 3.1 Satellite Monitoring System 3.1.1 Data Acquisition Pipeline python# Technical Implementation Requirements
- Sentinel-2 L2A products (10m resolution, 5-day revisit)
- Sentinel-1 SAR for cloud penetration (10m resolution)
- Landsat 8/9 as backup (30m resolution, 16-day revisit)
- Planet Labs API for high-value crops (3m daily - paid tier) Processing Requirements:
Automated cloud masking using Sen2Cor Atmospheric correction algorithms Time-series analysis for change detection Field boundary detection using AI segmentation
3.1.2 Vegetation Indices Calculation
NDVI (Normalized Difference Vegetation Index): Health monitoring NDWI (Normalized Difference Water Index): Irrigation needs EVI (Enhanced Vegetation Index): Crop vigor in dense canopy SAVI (Soil Adjusted Vegetation Index): Early growth stages Custom Indices: Crop-specific (wheat, tomatoes, potatoes, citrus)
3.1.3 Alert Generation System javascript// Alert Thresholds Configuration const alertRules = { ndvi_drop: { threshold: -0.15, // 15% drop from baseline urgency: 'high', message: 'مشكل في الحقل رقم {field_id} - تحقق من الري أو الأمراض', channels: ['whatsapp', 'sms', 'app'] }, harvest_prediction: { confidence: 0.85, days_before: 14, message: 'موعد الحصاد المتوقع: {date} - ابدأ في البحث عن مشترين' } } 3.2 WhatsApp Business Integration 3.2.1 Conversational AI Architecture Technology Stack:
WhatsApp Business API (via Twilio or direct Meta) Dialogflow CX or RASA for NLU (supports Darija training) Custom ML models for dialect understanding Voice message transcription (critical for low-literacy users)
Core Conversation Flows: yaml# Example Disease Diagnosis Flow
- Farmer: "طماطم مريضة" (tomatoes sick)
- Bot: "🌱 أرسل صورة للأوراق المصابة" (Send photo of affected leaves)
- [Image received]
- Bot: Processes with PlantNet API + custom model
- Bot: Returns:
- Disease name (Arabic/French)
- Severity score (1-5)
- Treatment protocol
- Local pesticide brands & doses
- Nearest supplier locations
- Prevention tips (voice note option) 3.2.2 Multi-Modal Communication
Text: Simple commands and queries Voice Notes: Complex questions (auto-transcribed) Images: Disease diagnosis, harvest quality checks Location: Field mapping, nearest services Documents: PDF reports, invoices, certificates
3.2.3 Proactive Messaging System python# Scheduled Advisory Messages daily_weather = { 'trigger': '06:00 local', 'condition': 'extreme_weather_forecast', 'message': 'تحذير: رياح قوية متوقعة - احم المحاصيل الحساسة' }
weekly_market = { 'trigger': 'Monday 08:00', 'message': 'أسعار السوق هذا الأسبوع:\n🍅 طماطم: {price} دج/كغ\n🥔 بطاطا: {price} دج/كغ' } 3.3 Marketplace Platform 3.3.1 Supply-Demand Matching Engine Algorithm Requirements:
Geo-spatial matching (minimize transport costs) Quality grade matching (A, B, C grades) Volume optimization (full truck loads) Price discovery mechanism (Dutch auction option) Reputation scoring (both parties)
Technical Implementation: javascript// Matching Algorithm Pseudocode function matchSupplyDemand(harvest, buyers) { return buyers .filter(b => b.crop === harvest.crop) .filter(b => b.quality >= harvest.quality) .filter(b => distance(b.location, harvest.location) < b.maxDistance) .map(b => ({ ...b, score: calculateMatchScore(b, harvest), transportCost: calculateTransportCost(b, harvest), netPrice: b.priceOffered - transportCost })) .sort((a, b) => b.score - a.score) .slice(0, 5); // Top 5 matches } 3.3.2 Digital Contract Management
Smart Contracts: Simple blockchain for high-value transactions Escrow Service: Payment held until delivery confirmed Quality Arbitration: Photo evidence, third-party verification Legal Compliance: Digital signatures valid under Algerian law
3.3.3 Logistics Coordination
Integration with logistics platform (from previous spec) Cold chain tracking for perishables Packaging material marketplace Export documentation support
3.4 Equipment Sharing Network 3.4.1 Asset Management System Equipment Categories:
Tractors (by horsepower: 50-75hp, 75-100hp, >100hp) Harvesters (grain, vegetable, fruit-specific) Irrigation systems (drip, sprinkler, pivot) Processing equipment (mills, presses, dryers) Transport vehicles (trucks, trailers)
Booking System: typescriptinterface EquipmentBooking { equipmentId: string; farmerId: string; startDate: Date; duration: number; // hours fieldLocation: GeoPoint; cropType: string; estimatedArea: number; // hectares pricePerHour: number; insuranceIncluded: boolean; operatorIncluded: boolean; } 3.4.2 Dynamic Pricing Model
Base rate by equipment type Seasonal adjustments (harvest peak pricing) Distance-based fees Loyalty discounts for regular users Group booking discounts (cooperative members)
3.5 Agricultural Intelligence System 3.5.1 Crop Planning Optimizer Input Variables:
Historical yield data Soil analysis (N-P-K, pH, organic matter) Water availability (rainfall + irrigation) Market price trends (3-year history) Climate projections
Output Recommendations:
Optimal crop mix for profitability Planting calendar by crop Expected ROI by crop/field Risk assessment (weather, market, disease)
3.5.2 Precision Agriculture Prescriptions python# Variable Rate Application Maps def generate_prescription_map(field_id, input_type): satellite_data = fetch_latest_imagery(field_id) soil_data = get_soil_analysis(field_id)
if input_type == 'fertilizer':
return calculate_variable_rate_npk(satellite_data, soil_data)
elif input_type == 'irrigation':
return calculate_irrigation_zones(satellite_data, weather_forecast)
elif input_type == 'seeding':
return optimize_seed_density(soil_data, variety_characteristics)
3.6 Financial Services Integration 3.6.1 Digital Payment System
Mobile Money: BaridiMob, CCP accounts Bank Integration: BADR (agricultural bank) Input Credit: Buy now, pay at harvest Crop Insurance: Parametric insurance based on satellite data
3.6.2 Subsidy Management
Automatic subsidy application filing Document verification system Direct subsidy disbursement tracking Compliance reporting for government
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Technical Architecture 4.1 System Architecture mermaidgraph TB subgraph "User Interfaces" WA[WhatsApp Bot] PWA[Progressive Web App] SMS[SMS Gateway] USSD[USSD Menu] end
subgraph "Core Services" API[API Gateway] ML[ML Pipeline] MATCH[Matching Engine] GEO[Geospatial Engine] end
subgraph "Data Layer" PG[PostgreSQL + PostGIS] TS[TimescaleDB] REDIS[Redis Cache] S3[Object Storage] end
subgraph "External Services" SAT[Satellite APIs] WEATHER[Weather APIs] PAY[Payment Gateways] GOV[Government APIs] end
WA --> API PWA --> API SMS --> API USSD --> API
API --> ML API --> MATCH API --> GEO
ML --> PG MATCH --> PG GEO --> TS
API --> REDIS ML --> S3
GEO --> SAT API --> WEATHER API --> PAY API --> GOV 4.2 Technology Stack 4.2.1 Backend Infrastructure yamlCore Stack: Language: Python 3.11 (FastAPI for APIs) Secondary: Node.js (Real-time features)
Databases: Primary: PostgreSQL 15 + PostGIS 3.3 Time-series: TimescaleDB Cache: Redis 7 Search: Elasticsearch (Arabic analyzer)
Message Queue: Celery + RabbitMQ (Heavy processing) BullMQ (Lightweight tasks)
ML/AI: Framework: PyTorch + TensorFlow Satellite: Rasterio, GDAL, Google Earth Engine NLP: Transformers (Arabic BERT models) Computer Vision: YOLO v8 for disease detection 4.2.2 Frontend Technologies yamlProgressive Web App: Framework: Next.js 14 (App Router) UI: Tailwind CSS + Shadcn/ui State: Zustand + React Query Maps: Mapbox GL (OpenStreetMap data) Charts: Recharts + D3.js
WhatsApp Bot: Platform: Twilio or WhatsApp Business API NLU: Dialogflow CX or RASA Voice: Whisper API for transcription
Mobile Considerations: Offline: Service Workers + IndexedDB Push: Web Push + Firebase SMS: Twilio + local providers 4.2.3 Satellite Data Pipeline python# Earth Engine + Custom Processing import ee import rasterio from sentinelsat import SentinelAPI
class SatelliteProcessor: def init(self): self.sentinel_api = SentinelAPI(user, password) ee.Initialize()
def fetch_imagery(self, aoi, date_range):
# Sentinel-2 for optical
s2 = ee.ImageCollection('COPERNICUS/S2_SR')
.filterBounds(aoi)
.filterDate(date_range[0], date_range[1])
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))
# Sentinel-1 for radar (cloud penetration)
s1 = ee.ImageCollection('COPERNICUS/S1_GRD')
.filterBounds(aoi)
.filterDate(date_range[0], date_range[1])
return self.process_collection(s2, s1)
def calculate_indices(self, image):
ndvi = image.normalizedDifference(['B8', 'B4'])
ndwi = image.normalizedDifference(['B3', 'B8'])
evi = image.expression(
'2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))',
{
'NIR': image.select('B8'),
'RED': image.select('B4'),
'BLUE': image.select('B2')
}
)
return image.addBands([ndvi, ndwi, evi])
4.3 Infrastructure Requirements 4.3.1 Compute Resources yamlProduction Environment: API Servers: - 3x (8 vCPU, 32GB RAM) - Auto-scaling 3-10 instances
ML Processing: - 2x (16 vCPU, 64GB RAM, GPU) - Batch processing for satellite imagery
Database: - Primary: 16 vCPU, 64GB RAM, 2TB SSD - Read Replicas: 2x (8 vCPU, 32GB RAM)
Cache: - Redis Cluster: 3 nodes (4GB each)
Storage:
- Object Storage: 10TB initial (satellite imagery)
- CDN: CloudFlare (African POPs)
Estimated Monthly Cost:
- Cloud (AWS/GCP): $2,500-3,500
- Or Local DC in Algeria: $1,500-2,000
4.3.2 Security Architecture
yamlSecurity Measures:
Authentication:
- Multi-factor for farmers (SMS OTP)
- OAuth 2.0 for API access
- JWT tokens with refresh
Data Protection: - AES-256 encryption at rest - TLS 1.3 for transit - Field-level encryption for PII
Compliance: - GDPR-compliant data handling - Right to deletion - Data residency options
Monitoring: - WAF for API protection - DDoS mitigation - Anomaly detection for fraud
- Implementation Roadmap 5.1 Phase 1: MVP (Months 1-4) Focus: Core WhatsApp bot + Basic satellite monitoring Deliverables:
WhatsApp registration and profile setup Disease diagnosis via photo Weather alerts and forecasts Basic NDVI monitoring for registered fields SMS fallback for alerts
Target: 50 farmers in Blida pilot 5.2 Phase 2: Market Integration (Months 5-8) Focus: Marketplace + Equipment sharing Deliverables:
Buyer-seller marketplace Price discovery and negotiation Digital contracts Equipment booking system Payment integration (BaridiMob)
Target: 500 farmers, 20 buyers 5.3 Phase 3: Intelligence Layer (Months 9-12) Focus: Advanced analytics + Financial services Deliverables:
Crop planning optimizer Yield prediction models Variable rate prescriptions Crop insurance integration Subsidy management
Target: 2,000 farmers across 3 wilayas 5.4 Phase 4: National Scale (Months 13-18) Focus: Geographic expansion + Government integration Deliverables:
Coverage in 10 major agricultural wilayas Government dashboard for monitoring Export market connections Advanced ML models trained on local data Multi-language support (add Tamazight)
Target: 10,000 farmers, 200 buyers
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Risk Assessment 6.1 Technical Risks RiskProbabilityImpactMitigationSatellite imagery cloud coverHighMediumUse SAR (radar) data as backupWhatsApp API restrictionsMediumHighBuild SMS + USSD fallbackPoor internet connectivityHighHighAggressive offline cachingLanguage model accuracy (Darija)MediumMediumContinuous training with local dataGPS accuracy in small fieldsLowMediumManual field boundary drawing 6.2 Business Risks RiskProbabilityImpactMitigationFarmer adoption resistanceHighHighFree first season, local championsMiddlemen retaliationMediumMediumGradual rollout, government backingSeasonal revenue fluctuationHighMediumSubscription model for stabilityGovernment policy changesLowHighStrong ministry relationships
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Success Metrics & KPIs 7.1 User Metrics
Monthly Active Farmers (MAF) Average session duration Feature adoption rate (satellite, market, equipment) User retention (6-month cohorts) NPS score
7.2 Agricultural Impact
Yield improvement per hectare (%) Input cost reduction (%) Post-harvest loss reduction (%) Time to market (days) Price premium achieved (%)
7.3 Business Metrics
GMV (Gross Merchandise Value) Take rate (commission %) CAC (Customer Acquisition Cost) LTV (Lifetime Value) Monthly Recurring Revenue
7.4 Social Impact
Farmer income improvement Youth employment in agriculture Women farmer participation CO₂ reduction from optimized inputs Water usage efficiency
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Budget Estimation 8.1 Development Costs (18 months) CategoryCost (USD)NotesDevelopment Team$360,0006 developers × $3,333/month × 18 monthsML/Data Scientists$180,0002 scientists × $5,000/month × 18 monthsAgronomists$72,0002 experts × $2,000/month × 18 monthsUI/UX Design$36,000Contract basisProject Management$54,0001 PM × $3,000/month × 18 monthsTotal Development$702,000 8.2 Operational Costs (Monthly) CategoryCost (USD)NotesInfrastructure$3,000Cloud + CDNSatellite Data$1,500Planet Labs subscriptionWhatsApp API$500Message costsSMS Gateway$800~20,000 messagesSupport Team$2,0004 agentsMarketing$1,500Digital + field activitiesTotal Monthly$9,300 8.3 Revenue Projections (Month 18) Revenue StreamMonthly (USD)AssumptionsTransaction Fees$25,0002.5% of $1M GMVPremium Subscriptions$10,0002,000 farmers × $5Equipment Commission$8,00010% of $80K bookingsData Services$5,000Government/NGO contractsInput Company Ads$3,000Sponsored recommendationsTotal Monthly Revenue$51,000
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Conclusion This AgTech platform represents a transformative opportunity to modernize Algerian agriculture while addressing critical national priorities. By leveraging satellite technology, AI, and mobile-first design, we can deliver immediate value to farmers while building a sustainable, scalable business. The phased approach minimizes risk while proving value at each stage. Starting with WhatsApp as the primary interface ensures immediate accessibility, while the progressive web app provides room for growth as users become more digitally sophisticated. Success depends on deep local partnerships, continuous iteration based on farmer feedback, and maintaining focus on solving real problems rather than imposing technology solutions.
Appendices A. Technology Vendor Comparison ServiceOption 1Option 2RecommendationSatellite DataSentinel HubGoogle Earth EngineGEE (free tier generous)WhatsApp APITwilioMeta DirectTwilio (easier setup)ML PlatformAWS SageMakerGoogle VertexVertex (better Arabic)MapsMapboxGoogle MapsMapbox (cost-effective) B. Pilot Program Structure
Duration: 3 months (one growing season) Location: Blida Province (diverse crops, close to Algiers) Farmers: 50 (mix of progressive and traditional) Crops: Tomatoes (30%), Potatoes (30%), Wheat (40%) Success Criteria: 15% yield improvement, 80% retention
C. Government Stakeholder Map
MADR (Ministry of Agriculture) - Primary partner MPTTN (Ministry of Post & Telecommunications) - Digital infrastructure Ministry of Commerce - Market regulations ANADE (National Agency for Entrepreneurship) - Funding Local Agricultural Directorates - Field implementation
Document Approval: RoleNameSignatureDateProject SponsorTechnical LeadAgricultural ExpertGovernment Liaison
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