Smart Pine Seedling Monitoring with Drone Imagery and Deep Learning

Effective plantation establishment relies on accurate, repeatable monitoring of seedling survival. Traditionally, this has relied on labour-intensive field surveys including walking plots, manually counting seedlings, and recording results by hand. While reliable, these methods are slow, costly, and difficult to scale.

Today, forest managers and consultants have a far more efficient alternative. By combining high-resolution drone imagery with deep learning models, we deliver a powerful solution for detecting and mapping seedlings rapidly, accurately, and across large plantation estates. This automated workflow not only reduces survey effort and cost but also provides clear, data-driven insights to guide replanting and forest management decisions.

High-Resolution Drone Mapping

We begin with aerial data collection using drones equipped with RGB cameras. Flights are planned to ensure consistent coverage and optimal lighting. The captured images are then processed into a georeferenced orthomosaic; a seamless, high-resolution map of the plantation with centimetre-level spatial accuracy.

This orthomosaic allows seedlings to be clearly identified, even at early growth stages. Whether your project spans 10 or >1,000 hectares, the drone can map the entire area efficiently enabling you to “see” every seedling from above with millimetre-scale clarity.

A DJI M300RTK drone and Zenmuse P1 camera payload used to capture plantation data

AI-Powered Seedling Detection

Once the orthomosaic is generated, our deep learning system automatically detects and localizes individual seedlings. Built on advanced object detection architecture and trained with tens of thousands of in-house labelled examples, the model recognizes seedlings across a wide range of conditions; including different growth stages, seasons, and vegetation backgrounds. Each seedling is detected and assigned a precise geographic coordinate, producing a spatially accurate seedling inventory.

What sets our system apart:

• Robust Training Data: Extensive labelling of seedling imagery across seasons and site conditions ensures reliable detection under real-world variability.

• High Accuracy: Independent benchmarks and internal validations show detection rates above 98%, matching or exceeding traditional field surveys.

• Scalability: The automated pipeline processes large areas in a fraction of the time, turning what once took days into hours.

• Flexible AI Framework: Our detection model is developed in-house, allowing us to quickly adapt it to new target species or vegetation types. By incorporating additional training data, we can extend the same deep learning pipeline to monitor different species, detect weeds, or assess other ecological indicators, making it a future-proof solution for a wide range of forest monitoring tasks.

Pine tree seedlings automatically detected with our deep learning algorithm

From Detection to Decisions: Mapping, Gaps & Replanting

The output is more than just a list of seedlings, it’s a fully geospatial map that highlights distribution patterns and density variations. Using built-in spatial analysis tools, we identify understocked or missed planting zones, enabling you to target replanting where it’s most needed.

You get clean, ready-to-use outputs such as GIS-compatible shapefiles, vegetation density maps, and statistical summaries ready for integration into your existing forest management tools.

Ready to Transform Your Seedling Monitoring?

Whether you're managing a commercial plantation or assessing post-fire regeneration, our drone + AI solution provides the speed, accuracy, and clarity you need to make better, faster management decisions.

With more than a decade of experience capturing survey-grade photogrammetry, LiDAR, and multispectral imagery across Australia, Multi Scan delivers the accurate, high-resolution data your project deserves. Get in touch, we’d love to help with your next survey.