CMTNet: a hybrid CNN-transformer network for UAV-based hyperspectral crop classification in precision agriculture
Abstract Hyperspectral imaging acquired from unmanned aerial vehicles (UAVs) offers detailed spectral and spatial data that holds transformative potential for precision agriculture applications, such as crop classification, health monitoring, and yield estimation. However, traditional methods strugg...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-97052-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849726222830927872 |
|---|---|
| author | Xihong Guo Quan Feng Faxu Guo |
| author_facet | Xihong Guo Quan Feng Faxu Guo |
| author_sort | Xihong Guo |
| collection | DOAJ |
| description | Abstract Hyperspectral imaging acquired from unmanned aerial vehicles (UAVs) offers detailed spectral and spatial data that holds transformative potential for precision agriculture applications, such as crop classification, health monitoring, and yield estimation. However, traditional methods struggle to effectively capture both local and global features, particularly in complex agricultural environments with diverse crop types, varying growth stages, and imbalanced data distributions. To address these challenges, we propose CMTNet, an innovative deep learning framework that integrates convolutional neural networks (CNNs) and Transformers for hyperspectral crop classification. The model combines a spectral-spatial feature extraction module to capture shallow features, a dual-branch architecture that extracts both local and global features simultaneously, and a multi-output constraint module to enhance classification accuracy through cross-constraints among multiple feature levels. Extensive experiments were conducted on three UAV-acquired datasets: WHU-Hi-LongKou, WHU-Hi-HanChuan, and WHU-Hi-HongHu. The experimental results demonstrate that CMTNet achieved overall accuracy (OA) values of 99.58%, 97.29%, and 98.31% on these three datasets, surpassing the current state-of-the-art method (CTMixer) by 0.19% (LongKou), 1.75% (HanChuan), and 2.52% (HongHu) in OA values, respectively. These findings indicate its superior potential for UAV-based agricultural monitoring in complex environments. These results advance the precision and reliability of hyperspectral crop classification, offering a valuable solution for precision agriculture challenges. |
| format | Article |
| id | doaj-art-e4931fb8f7c84a0cbbbf226e783f2990 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e4931fb8f7c84a0cbbbf226e783f29902025-08-20T03:10:14ZengNature PortfolioScientific Reports2045-23222025-04-0115111810.1038/s41598-025-97052-wCMTNet: a hybrid CNN-transformer network for UAV-based hyperspectral crop classification in precision agricultureXihong Guo0Quan Feng1Faxu Guo2Dingxi Sanniu Agricultural Machinery Manufacturing Co., Ltd.College of Mechanical and Electrical Engineering, Gansu Agriculture UniversityCollege of Mechanical and Electrical Engineering, Gansu Agriculture UniversityAbstract Hyperspectral imaging acquired from unmanned aerial vehicles (UAVs) offers detailed spectral and spatial data that holds transformative potential for precision agriculture applications, such as crop classification, health monitoring, and yield estimation. However, traditional methods struggle to effectively capture both local and global features, particularly in complex agricultural environments with diverse crop types, varying growth stages, and imbalanced data distributions. To address these challenges, we propose CMTNet, an innovative deep learning framework that integrates convolutional neural networks (CNNs) and Transformers for hyperspectral crop classification. The model combines a spectral-spatial feature extraction module to capture shallow features, a dual-branch architecture that extracts both local and global features simultaneously, and a multi-output constraint module to enhance classification accuracy through cross-constraints among multiple feature levels. Extensive experiments were conducted on three UAV-acquired datasets: WHU-Hi-LongKou, WHU-Hi-HanChuan, and WHU-Hi-HongHu. The experimental results demonstrate that CMTNet achieved overall accuracy (OA) values of 99.58%, 97.29%, and 98.31% on these three datasets, surpassing the current state-of-the-art method (CTMixer) by 0.19% (LongKou), 1.75% (HanChuan), and 2.52% (HongHu) in OA values, respectively. These findings indicate its superior potential for UAV-based agricultural monitoring in complex environments. These results advance the precision and reliability of hyperspectral crop classification, offering a valuable solution for precision agriculture challenges.https://doi.org/10.1038/s41598-025-97052-wHyperspectral imagingCrop classificationMulti-output feature constraintsConvolutional neural networksTransformer |
| spellingShingle | Xihong Guo Quan Feng Faxu Guo CMTNet: a hybrid CNN-transformer network for UAV-based hyperspectral crop classification in precision agriculture Scientific Reports Hyperspectral imaging Crop classification Multi-output feature constraints Convolutional neural networks Transformer |
| title | CMTNet: a hybrid CNN-transformer network for UAV-based hyperspectral crop classification in precision agriculture |
| title_full | CMTNet: a hybrid CNN-transformer network for UAV-based hyperspectral crop classification in precision agriculture |
| title_fullStr | CMTNet: a hybrid CNN-transformer network for UAV-based hyperspectral crop classification in precision agriculture |
| title_full_unstemmed | CMTNet: a hybrid CNN-transformer network for UAV-based hyperspectral crop classification in precision agriculture |
| title_short | CMTNet: a hybrid CNN-transformer network for UAV-based hyperspectral crop classification in precision agriculture |
| title_sort | cmtnet a hybrid cnn transformer network for uav based hyperspectral crop classification in precision agriculture |
| topic | Hyperspectral imaging Crop classification Multi-output feature constraints Convolutional neural networks Transformer |
| url | https://doi.org/10.1038/s41598-025-97052-w |
| work_keys_str_mv | AT xihongguo cmtnetahybridcnntransformernetworkforuavbasedhyperspectralcropclassificationinprecisionagriculture AT quanfeng cmtnetahybridcnntransformernetworkforuavbasedhyperspectralcropclassificationinprecisionagriculture AT faxuguo cmtnetahybridcnntransformernetworkforuavbasedhyperspectralcropclassificationinprecisionagriculture |