SWNet: A Cross-Spectral Network for Camouflaged Weed Detection

2026-04-17

AI Summary

This work addresses the challenge of accurately detecting camouflaged weeds in dense agricultural fields, where high phenotypic similarity between crops and weeds limits the performance of conventional vision-based methods. To this end, the authors propose an end-to-end dual-modality cross-spectral network that fuses visible and near-infrared (NIR) imagery. Leveraging Pyramid Vision Transformer v2 as the backbone to capture long-range dependencies, the model incorporates a gated fusion module for dynamic integration of multispectral features and an edge-aware refinement module to enhance boundary precision. By exploiting chlorophyll-related reflectance differences in the NIR spectrum to improve discriminability, the proposed method achieves state-of-the-art performance on the Weeds-Banana dataset, significantly outperforming ten leading approaches in high-accuracy segmentation of camouflaged weeds.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

This paper presents SWNet, a bimodal end-to-end cross-spectral network specifically engineered for the detection of camouflaged weeds in dense agricultural environments. Plant camouflage, characterized by homochromatic blending where invasive species mimic the phenotypic traits of primary crops, poses a significant challenge for traditional computer vision systems. To overcome these limitations, SWNet utilizes a Pyramid Vision Transformer v2 backbone to capture long-range dependencies and a Bimodal Gated Fusion Module to dynamically integrate Visible and Near-Infrared information. By leveraging the physiological differences in chlorophyll reflectance captured in the NIR spectrum, the proposed architecture effectively discriminates targets that are otherwise indistinguishable in the visible range. Furthermore, an Edge-Aware Refinement module is employed to produce sharper object boundaries and reduce structural ambiguity. Experimental results on the Weeds-Banana dataset indicate that SWNet outperforms ten state-of-the-art methods. The study demonstrates that the integration of cross-spectral data and boundary-guided refinement is essential for high segmentation accuracy in complex crop canopies. The code is available on GitHub: https://cod-espol.github.io/SWNet/

Problem

• camouflaged weed detection
• cross-spectral imaging
• plant camouflage
• agricultural vision
• weed segmentation

Innovation

• cross-spectral fusion
• camouflaged weed detection
• bimodal gated fusion
• edge-aware refinement
• Pyramid Vision Transformer