Projects

A Collaboration with
Green Tech ProjectsSeedling Detection and Empty Cavity Counting
Apr 2026
Development


GreenTech Projects partnered with Saiwa to develop an AI-powered computer vision workflow for automated seedling tray analysis using drone imagery. The project focused on detecting and localizing seedling blocks, identifying empty cavities within propagation trays, and improving the speed and consistency of nursery monitoring operations. By combining aerial imagery with machine learning–based image analysis, the project demonstrated the potential of automated nursery assessment for large-scale propagation environments.
The solution utilized RGB drone imagery collected over seedling fields and propagation areas. Saiwa developed and trained custom machine learning models capable of identifying tray structures and detecting empty cells across nursery blocks. The project was successfully completed and validated the feasibility of applying AI-based image processing to greenhouse and nursery production workflows
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Ordered by
Ducks Unlimited Canada (DUC)Detection and Semantic Segmentation of European Water Chestnut Colonies in Drone Orthophotos
Jan 2026
Development


In 2026, Saiwa continues its collaboration with Ducks Unlimited Canada (DUC) to advance drone-based monitoring of invasive aquatic vegetation. Building on previous phases focused on segmentation accuracy and geospatial localization, this phase introduces enhanced pixel-level semantic segmentation of European Water Chestnut (EWC) to enable detailed mapping of both individual plants and dense colonies across diverse aquatic environments.
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Ordered by
Ducks Unlimited Canada (DUC)Multispectral Submersion Classification of Water Soldier in Drone Imagery
Jan 2026
Development


In 2026, Saiwa continues its collaboration with Ducks Unlimited Canada (DUC) to further enhance drone-based monitoring of invasive aquatic vegetation. Building on last year’s high-accuracy RGB-based Water Soldier detection pipeline, this new phase focuses specifically on determining whether each detected plant is located above or below the water surface.
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This article introduces a computer vision–based bat monitoring solution developed by Saiwa in collaboration with Sam Watson Ecology. The system analyzes images and videos captured from residential and building environments to detect bats and their nests. The goal is to deliver one of the first commercial ecological monitoring solutions that is accurate, cost-effective, and easy to deploy for environmental agencies, researchers, and industry partners.
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