Acoustic-Hyperspectral Multimodal Generation and Fusion for Cotton Drought Stress Detection Based on UAV Remote Sensing

With the growing demand for early monitoring of crop moisture in precision agriculture continues to grow, this article proposes a deep detection framework that integrates acoustic

emission signals with hyperspectral images to address the limitations

of single-modal characterization and susceptibility to noise

interference in detecting drought stress in cotton. The study first

constructs a multiscale time–frequency branch and a regional–

global spectral branch to capture, respectively, the transient pulse

characteristics induced by cavitation and the long-range dependencies

of the canopy spectrum. Subsequently, a dual second-order

attention module is introduced to enhance edge information and

spatial high-frequency details through the synergistic use of channel

gradients and structural tensors. Finally, a deep adaptive fusion

mechanism is designed to achieve dynamic weighted allocation

of bimodal features. Experiments show that the model achieves

an overall accuracy of 97.01%, significantly outperforming the

baselinemodels, even in themore challenging task of early detection

of mild drought, the model maintains a high accuracy of 95.47%.

In summary, the method proposed in this article provides a highly

robust technical approach for the nondestructive diagnosis of crop

water deficit.