AI Model for Forest Disease Detection

A computer vision model trained on satellite imagery to detect forest health anomalies.

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The Challenge

Develop a reliable system for detecting forest disease outbreaks using satellite imagery. The tool had to process high-resolution visual data across vast areas and identify subtle patterns indicating multiple types of forest degradation. Accuracy, scalability, and clear visualization were essential to support timely decision-making for environmental teams.

Overview

Industry
Environmental AI
Duration
4 months
Total project hours
880
Technology
Python, TensorFlow, OpenCV
Integrations
Sentinel-2 API

The Solution

We designed and trained a convolutional neural network (CNN) pipeline capable of multi-label classification across large satellite datasets. The model was optimized to detect various indicators of forest disease, including discoloration, canopy thinning, and abnormal texture formations.

To make insights actionable, we deployed a web-based dashboard that visualizes the model's predictions in real time, allowing researchers and forestry teams to monitor risk zones, track disease progression, and export data for field validation. The solution was built with scalability in mind and integrates seamlessly with existing geospatial analysis workflows.