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Computer Vision-Based Biomass Estimation for Invasive Plants
Published on: February 9, 2024
Hybrid CNN-GNN architectures with distributed training for heathland plant classification
Babatoundé Moctard Olouladé1, Jesper Leth Bak1, Peter Borgen Sørensen1
1Department of Ecoscience, Aarhus University, C.F. Møllers Alle, Aarhus, 8000, Denmark.
Environmental Monitoring and Assessment
|June 5, 2026
Summary
PlantGraphNet, a new hybrid framework, improves heathland plant species classification by combining visual and structural data. This approach offers higher accuracy and interpretability than traditional methods for ecological monitoring.
Area of Science:
- Ecology
- Computer Science
- Artificial Intelligence
Background:
- Accurate heathland plant species identification is vital for biodiversity assessment and ecological monitoring.
- Convolutional Neural Networks (CNNs) excel at visual feature extraction but struggle with irregular spatial relationships in vegetation.
- Existing Graph Neural Network (GNN) approaches often use simplified graph structures, neglecting plant morphology.
Purpose of the Study:
- To develop a novel hybrid CNN-GNN framework, PlantGraphNet, for enhanced heathland plant species classification.
- To integrate visual features with structural plant information for improved accuracy and interpretability.
- To address the limitations of existing methods in capturing complex spatial relationships in heathland ecosystems.
Main Methods:
- PlantGraphNet constructs graphs using image-derived keypoints and local descriptors, representing plant regions as nodes and spatial relationships as edges.
- A CNN backbone extracts appearance features, while graph convolution and attention layers process relational context.
- Distributed data-parallel training ensures scalability and efficient gradient synchronization for large datasets.
Main Results:
- PlantGraphNet achieved a precision of 98.98% on Danish aerial heathland datasets, significantly outperforming CNN-only models.
- The hybrid approach successfully integrated visual and structural data for robust classification.
- Explicit graph construction enhanced the interpretability of classification outcomes by linking them to plant structures.
Conclusions:
- Integrating CNN-extracted features with graph representations of plant morphology offers a powerful and interpretable method for fine-grained plant classification.
- PlantGraphNet provides a scalable and accurate solution for ecological monitoring and biodiversity assessment in heathland environments.
- This framework advances the application of AI in ecological studies by capturing complex spatial and structural information.