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Avail Agri
Digital Agriculture Solutions

Smart Farming Technology

Advanced digital technologies transforming agriculture through data-driven insights, precision application, and intelligent automation for sustainable productivity.

22%
Input Efficiency
Improving
12%
Yield Improvement
Stable
35%
Time Savings
Improving
5x
Decision Speed
Improving

Digital Solutions Portfolio

Comprehensive suite of digital agriculture solutions providing real-time insights, predictive analytics, and precision control for modern farming operations.

Farm Management Platform
Unlimited fields

FieldView Pro

Comprehensive digital platform integrating field data, analytics, and decision support for precision agriculture

Key Features:
Real-time monitoring
Predictive analytics
Field mapping
Yield optimization
Integration
Equipment, sensors, weather, satellite
Data Retention
10 years
Crop Monitoring
Field to region scale

CropScout AI

AI-powered crop monitoring using satellite imagery and drone data for early problem detection and intervention

Key Features:
Disease detection
Pest identification
Stress analysis
Growth tracking
Integration
Satellite feeds, drone data, IoT sensors
Data Retention
5 years
Application Technology
Equipment specific

PrecisionSpray

Variable rate application system with real-time optimization for precise input delivery and waste reduction

Key Features:
Variable rate control
Real-time adjustment
Application mapping
Drift prevention
Integration
Sprayer systems, GPS, weather stations
Data Retention
3 years
Climate Intelligence
1km resolution

WeatherWise

Hyperlocal weather forecasting and climate risk assessment for improved farm decision making

Key Features:
Microclimate forecasts
Risk alerts
Spray timing
Harvest optimization
Integration
Weather networks, satellite data
Data Retention
Historical data included

Core Technologies

Foundation technologies powering our digital agriculture solutions, from satellite imagery to AI-driven analytics and IoT sensor networks.

Satellite Imagery

High-resolution satellite data for crop monitoring and field analysis

Capabilities:
NDVI analysis
Stress detection
Growth monitoring
Historical comparison
Resolution:3-10 meter pixels
Frequency:Daily to weekly updates
Coverage:Global

IoT Sensors

Network of connected sensors providing real-time field condition data

Capabilities:
Soil moisture
Temperature
Humidity
Nutrient levels
Resolution:Point measurements
Frequency:Continuous monitoring
Coverage:Field network

AI & Machine Learning

Advanced algorithms for pattern recognition and predictive analytics

Capabilities:
Predictive modeling
Pattern recognition
Anomaly detection
Optimization
Resolution:Variable analysis scales
Frequency:Real-time processing
Coverage:Data-dependent

Mobile Applications

User-friendly mobile interfaces for field data collection and management

Capabilities:
Data collection
Real-time alerts
Field navigation
Reporting
Resolution:User interface
Frequency:On-demand access
Coverage:Field to farm

Benefits & Returns

Quantifiable benefits and return on investment from digital agriculture adoption across productivity, efficiency, and sustainability metrics.

Precision Application

Variable rate application reducing input costs by 15-25%

Key Metrics:
Input savings: 15-25%
Application accuracy: +90%
Waste reduction: 30%
Impact
Cost reduction and environmental benefit

Early Detection

AI-powered monitoring detecting issues 7-14 days earlier

Key Metrics:
Detection speed: 7-14 days earlier
Accuracy: 85-95%
Coverage: 100% field
Impact
Reduced crop losses and treatment costs

Yield Optimization

Data-driven decisions increasing yields by 8-15%

Key Metrics:
Yield increase: 8-15%
Efficiency gain: 20%
ROI: 3-5x
Impact
Improved profitability and productivity

Risk Management

Weather intelligence and predictive modeling reducing weather risks

Key Metrics:
Risk reduction: 40%
Forecast accuracy: 85%
Alert time: 3-7 days
Impact
Better planning and risk mitigation

Data Integration

Comprehensive data collection and integration from multiple sources providing complete agricultural intelligence for informed decision making.

Environmental Data

Data Sources:
Weather stationsSatellite imagerySoil sensorsClimate models
Parameters:
Temperature
Precipitation
Humidity
Solar radiation
+1 more parameters
Frequency:Real-time to daily
Applications:
Risk assessmentTiming decisionsClimate adaptation

Crop Data

Data Sources:
DronesSatellitesField sensorsScouting reports
Parameters:
Growth stage
Health status
Stress indicators
Yield estimates
Frequency:Weekly to monthly
Applications:
Crop monitoringProblem detectionYield prediction

Soil Data

Data Sources:
Soil sensorsGrid samplingPenetrometersLaboratory analysis
Parameters:
Moisture
Nutrients
pH
Compaction
+1 more parameters
Frequency:Continuous to seasonal
Applications:
Nutrient managementIrrigation controlSoil health

Equipment Data

Data Sources:
Machinery sensorsGPS systemsApplication equipmentTelematics
Parameters:
Position
Speed
Application rates
Performance metrics
Frequency:Real-time
Applications:
Precision applicationEfficiency optimizationMaintenance

Implementation Process

Structured approach to digital agriculture adoption with comprehensive support from assessment through optimization and ongoing improvement.

1

Digital Assessment

Evaluate current technology and identify digital opportunities

Duration: 2-4 weeks
Key Activities:
Technology audit
Needs assessment
Infrastructure evaluation
ROI analysis
Deliverables:
Digital readiness report
Implementation roadmap
Technology recommendations
2

Platform Setup

Deploy core digital platforms and establish data connections

Duration: 4-8 weeks
Key Activities:
Platform installation
Data integration
Sensor deployment
User training
Deliverables:
Operational platform
Data dashboards
Training materials
Support protocols
3

Data Collection

Begin systematic data collection and establish baseline metrics

Duration: 1 growing season
Key Activities:
Sensor monitoring
Field data collection
Satellite integration
Performance tracking
Deliverables:
Baseline data
Field maps
Performance metrics
Initial insights
4

Optimization

Analyze data patterns and optimize management practices

Duration: Ongoing
Key Activities:
Data analysis
Pattern recognition
Practice refinement
Continuous improvement
Deliverables:
Optimized practices
Predictive models
Performance improvements
ROI validation

Transform Your Farm with Digital Technology

Join thousands of farmers leveraging digital agriculture to increase productivity, reduce costs, and build sustainable farming operations.