August 11, 2025Open Access

MF-FusionNet: A Lightweight Multimodal Network for Monitoring Drought Stress in Winter Wheat Based on Remote Sensing Imagery

QGQiang GuoKunming University of Science and Technology BHBo HanXinjiang Agricultural University PCPengyu ChuNational Tsing Hua University

Key Points

MF-FusionNet achieved 96.71% accuracy for identifying drought-affected winter wheat areas, improving single-modal methods.
A Dual-Coordinate Attention Feature Extraction module was employed to enhance feature representation, resulting in better model performance.
The method integrated various features through a Cross-Stage Feature Fusion Strategy, addressing scale and semantic differences.
Results indicate potential for real-time monitoring of drought stress in agriculture, though model validation in diverse environments remains necessary.

Abstract

To improve the identification of drought-affected areas in winter wheat, this paper proposes a lightweight network called MF-FusionNet based on multimodal fusion of RGB images and vegetation indices (NDVI and EVI). A multimodal dataset covering various drought levels in winter wheat was constructed. To enable deep fusion of modalities, a Lightweight Multimodal Fusion Block (LMFB) was designed, and a Dual-Coordinate Attention Feature Extraction module (DCAFE) was introduced to enhance semantic feature representation and improve drought region identification. To address differences in scale and semantics across network layers, a Cross-Stage Feature Fusion Strategy (CFFS) was proposed to integrate multi-level features and enhance overall performance. The effectiveness of each module was validated through ablation experiments. Compared to traditional single-modal methods, MF-FusionNet achieved higher accuracy, recall, and F1-score—improved by 1.35%, 1.43%, and 1.29%, respectively—reaching 96.71%, 96.71%, and 96.64%. A basis for real-time monitoring and precise irrigation management under winter wheat drought stress was provided by this study.

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