4000+
Ground-Truth Data Points
150+
Aquaculture Ponds
80%+ Recall
Unsafe Pond Detection
Satellite-Based
Remote Pond Monitoring
Industry
Aquaculture · Fisheries
Farm Advisory · Environmental Monitoring
Business Function
Capability
Remote Sensing · Water Quality Prediction · Risk Classification · Decision Support
Tech Stack
Sentinel-1 · Sentinel-2 · ERA5 Weather Data · Machine Learning · GIS Analytics
Overview
Across Asia and Africa, millions of aquaculture farmers rely on open ponds where water quality directly determines productivity, feed efficiency, disease risk, and fish survival. Yet most ponds are still managed using visual inspection, occasional test kits, or laboratory testing—approaches that provide only limited visibility and are difficult to scale.
Hornbill AG developed PondSense to provide a scalable alternative. By combining satellite imagery, weather intelligence, and machine learning, the platform continuously monitors pond conditions, identifies emerging water quality risks, and helps farmers prioritize interventions before visible signs of stress appear.
Challenge
Traditional Pond Monitoring
Why It Falls Short
Problems are often detected only after productivity declines.
Visual inspection and farmer intuition
Capture only a single point in time and require manual effort.
Periodic test kits
Accurate but expensive, slow, and impractical for routine monitoring.
Laboratory analysis
Continuous but difficult to scale due to hardware, connectivity, and maintenance costs.
IOT sensors
Our Solution
Multi-Source Environmental Intelligence
PondSense integrates freely available Sentinel satellite imagery with ERA5 weather observations and farm context to estimate pond-scale water quality remotely. Surface temperature, optical reflectance, spectral indices, and local weather patterns are aligned with historical ground-truth measurements to build predictive models for critical water quality parameters.
From Prediction to Action
Rather than replacing farmers' expertise, PondSense prioritizes attention where it is needed most. The platform classifies ponds as stable or at risk, prompting targeted field validation and delivering practical recommendations such as increasing aeration, adjusting feeding schedules, or conducting focused water quality tests. This shifts pond management from reactive to proactive decision-making.
Impact Delivered
Early Risk Identification
Models identify unsafe pond conditions with more than 80% recall, enabling earlier intervention before productivity losses become visible.
Scalable Remote Monitoring
Provides pond-level environmental intelligence without deploying expensive sensors or laboratory infrastructure.
Improved Field Prioritization
Helps farmers, extension workers, and program teams focus monitoring efforts on ponds with the highest predicted risk, improving the efficiency of field visits and testing.
Built for Operational Decision Making
Combines satellite intelligence, weather dynamics, and machine learning into an actionable monitoring workflow designed for real-world aquaculture operations rather than research environments.
