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

Qiang Guo, Bo Han, Pengyu Chu, Yiping Wan and Jingjing Zhang ()
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Qiang Guo: College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Bo Han: College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Pengyu Chu: College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Yiping Wan: College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Jingjing Zhang: College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China

Agriculture, 2025, vol. 15, issue 15, 1-28

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.

Keywords: winter wheat; drought stress; multimodal feature fusion; remote sensing; vegetation indices (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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