This Case Shows Specific Expertise. Find the Companies with the Skills Your Project Demands!

You're viewing one of tens of thousands of real cases compiled on Many.dev. Each case demonstrates specific, tangible expertise.

But how do you find the company that possesses the exact skills and experience needed for your project? Forget generic filters!

Our unique AI system allows you to describe your project in your own words and instantly get a list of companies that have already successfully applied that precise expertise in similar projects.

Create a free account to unlock powerful AI-powered search and connect with companies whose expertise directly matches your project's requirements.

Development of Scalable Machine Learning System for Drone-Based Tree Health Monitoring

beetroot

Environmental Services

Agriculture

Information technology

Operational Scaling Challenges in Reforestation Monitoring

Existing manual and semi-automated processes for tree health monitoring using drone imagery lacked scalability and precision. Current systems required significant human intervention, limited real-time data processing capabilities, and couldn't provide granular metrics at scale for tree health, growth rates, and geolocation tracking.

About the Client

Global reforestation company operating in 25 countries since 2013, specializing in technology-driven afforestation solutions

Key Project Goals

Develop automated ML algorithms for drone image analysis
Implement scalable tree health monitoring system
Enable real-time growth metrics tracking
Create geospatial visualization platform
Support business expansion through enhanced service offerings

Core System Requirements

Automated drone image processing pipeline
Tree species classification using computer vision
Health metrics calculation (growth rate, leaf density, stress indicators)
Geolocation tagging and mapping system
Real-time dashboard for client data access
API integration for third-party platform compatibility

Technology Stack Requirements

AWS

MongoDB

Python

QGIS

TensorFlow/PyTorch

System Integration Needs

Drone/UAV data acquisition systems
Client's existing mapping infrastructure
Third-party environmental monitoring platforms
Project management and billing systems

Performance and Quality Attributes

High-throughput image processing capabilities
99.9% system availability for client access
Data security and privacy compliance (GDPR)
Scalable architecture for global operations
Transparent reporting and audit trail system

Expected Business Impact

Enables expansion of service offerings to include real-time tree tracking and advanced analytics, increases operational efficiency by reducing manual labor requirements by 70%, improves client retention through enhanced data accuracy, and supports entry into new geographic markets through scalable infrastructure.