MF-FusionNet: A Lightweight Multimodal Network for Monitoring Drought Stress in Winter Wheat Based on Remote Sensing Imagery
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....
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/15/1639 |
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| author | Qiang Guo Bo Han Pengyu Chu Yiping Wan Jingjing Zhang |
| author_facet | Qiang Guo Bo Han Pengyu Chu Yiping Wan Jingjing Zhang |
| author_sort | Qiang Guo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-22e744ca4eab4c1d99b4981a9ce0b34c |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-22e744ca4eab4c1d99b4981a9ce0b34c2025-08-20T03:36:30ZengMDPI AGAgriculture2077-04722025-07-011515163910.3390/agriculture15151639MF-FusionNet: A Lightweight Multimodal Network for Monitoring Drought Stress in Winter Wheat Based on Remote Sensing ImageryQiang Guo0Bo Han1Pengyu Chu2Yiping Wan3Jingjing Zhang4College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaTo 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.https://www.mdpi.com/2077-0472/15/15/1639winter wheatdrought stressmultimodal feature fusionremote sensingvegetation indices |
| spellingShingle | Qiang Guo Bo Han Pengyu Chu Yiping Wan Jingjing Zhang MF-FusionNet: A Lightweight Multimodal Network for Monitoring Drought Stress in Winter Wheat Based on Remote Sensing Imagery Agriculture winter wheat drought stress multimodal feature fusion remote sensing vegetation indices |
| title | MF-FusionNet: A Lightweight Multimodal Network for Monitoring Drought Stress in Winter Wheat Based on Remote Sensing Imagery |
| title_full | MF-FusionNet: A Lightweight Multimodal Network for Monitoring Drought Stress in Winter Wheat Based on Remote Sensing Imagery |
| title_fullStr | MF-FusionNet: A Lightweight Multimodal Network for Monitoring Drought Stress in Winter Wheat Based on Remote Sensing Imagery |
| title_full_unstemmed | MF-FusionNet: A Lightweight Multimodal Network for Monitoring Drought Stress in Winter Wheat Based on Remote Sensing Imagery |
| title_short | MF-FusionNet: A Lightweight Multimodal Network for Monitoring Drought Stress in Winter Wheat Based on Remote Sensing Imagery |
| title_sort | mf fusionnet a lightweight multimodal network for monitoring drought stress in winter wheat based on remote sensing imagery |
| topic | winter wheat drought stress multimodal feature fusion remote sensing vegetation indices |
| url | https://www.mdpi.com/2077-0472/15/15/1639 |
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