Skip to main content

The European Water Chestnut is an invasive plant disrupting Ontario’s water habitats. It’s listed as a prohibited species to help with its management. Ducks Unlimited Canada is working to eliminate this plant in eastern Ontario through monitoring and control efforts. The project aims to enhance these efforts using drone surveillance and machine learning for more efficient plant classification and mapping, aiding in the detection and management of current and emerging infestations.

Problem: The European Water Chestnut (Trapa natans), an invasive aquatic plant with floating leaves, is a serious threat to Ontario’s aquatic ecosystems. It competes with native species and disrupts the ecological balance of the area. The manual review of approximately 1,500 images by Ducks Unlimited Canada staff for identifying this species was an extremely time-consuming process, requiring at least 50 hours of work for each field day.

Solution: Saiwa Company developed an AI-based software capable of operating in offline conditions. This machine learning service can automatically identify the European Water Chestnut in images captured by drones.

Outcome: The project led to a significant reduction in human error and the time required to identify the European Water Chestnut in images. This allowed Ducks Unlimited Canada to more efficiently monitor and control existing populations and prevent the establishment of new ones, thereby strengthening the protection of Ontario’s aquatic ecosystems.

Read Also: Advancing UAV-based EWC surveillance

Project customer: Duck Unlimited Canada Company

For more information please contact us via: info@saiwa.ai

Related Projects

    Phragmites Detector – AI-Powered Invasive Species Monitoring in Wetlands
    Phragmites Detector is an AI-driven system that uses drone imagery and adaptive machine learning to identify and track invasive Phragmites in wetlands. Designed for conservation groups, it offers scalable, accurate, and user-friendly tools for early detection, monitoring, and ecological reporting.
    Detection and Semantic Segmentation of European Water Chestnut Colonies in Drone Orthophotos
    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.
    Detect Water Soldier RGB Drone Monitoring And Tracking
    In late 2024, Saiwa Co and Ducks Unlimited Canada launched a project to enhance UAV-based detection and surveillance of the invasive aquatic plant Water Soldier (Stratiotes Aloides) using advanced image processing and deep learning. The team addressed challenges from dense and submerged vegetation, environmental noise, and irregular plant shapes by reformulating the task as semantic segmentation. The project delivered a robust model achieving 95.52% pixel accuracy, enabling accurate monitoring of both isolated plants and dense colonies, supporting early intervention, efficient management, and a scalable foundation for future multispectral enhancements.
    Multispectral Submersion Classification of Water Soldier in Drone Imagery
    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.
    Advancing UAV-based EWC surveillance
    To enhance Ducks Unlimited Canada (DUC)‘s capability for UAV-based surveillance of European Water Chestnut (EWC) through machine learning, we at Saiwa have previously implemented the initial version of the EWC detector software. In the second stage, we are in the process of finalizing the product’s features and upgrading its interfaces.The two primary features to be incorporated in this stage are as follows: Incremental learning for gradually training the deep network over time. This feature enables us to rectify false positive and false negative detections over time. Reporting the 3D universal coordinates of EWC locations using drone configuration and temporal GPS data.
    AI Powered Date Palm Monitoring & Analysis
    Hemaa.ai, in partnership with Tarmeez, delivers an AI-powered solution for date palm plantations that enables early health monitoring, precise tree counting, and targeted weed detection using drone and satellite imagery. Leveraging advanced deep learning models, this platform enhances operational efficiency, reduces costs, and promotes sustainable agriculture through timely alerts and accurate spatial data mapping.