Lightweight Multispectral Crop-Weed Segmentation for Precision Agriculture

• URL: http://arxiv.org/abs/2505.07444v1
• Date: Mon, 12 May 2025 11:08:42 GMT
• Title: Lightweight Multispectral Crop-Weed Segmentation for Precision Agriculture
• Authors: Zeynep Galymzhankyzy, Eric Martinson,
• Abstract summary: CNN-based methods struggle to generalize and rely on RGB imagery, limiting performance under complex field conditions.<n>We propose a lightweight transformer-CNN hybrid that processes RGB, Near-Infrared (NIR), and Red-Edge (RE) bands using specialized encoders and dynamic modality integration.<n>The model achieves a segmentation accuracy (mean IoU) of 78.88%, outperforming RGB-only models by 15.8 percentage points.
• Score: 1.2277343096128712
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficient crop-weed segmentation is critical for site-specific weed control in precision agriculture. Conventional CNN-based methods struggle to generalize and rely on RGB imagery, limiting performance under complex field conditions. To address these challenges, we propose a lightweight transformer-CNN hybrid. It processes RGB, Near-Infrared (NIR), and Red-Edge (RE) bands using specialized encoders and dynamic modality integration. Evaluated on the WeedsGalore dataset, the model achieves a segmentation accuracy (mean IoU) of 78.88%, outperforming RGB-only models by 15.8 percentage points. With only 8.7 million parameters, the model offers high accuracy, computational efficiency, and potential for real-time deployment on Unmanned Aerial Vehicles (UAVs) and edge devices, advancing precision weed management.

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