As an expert commentator, I find the proposed DedustNet to be a significant contribution towards improving the performance and reliability of automated agricultural machines in dusty environments. The use of Swin Transformer-based units in wavelet networks for agricultural image dusting is a novel approach that shows promise in addressing the challenges posed by dust in agricultural settings.

The introduction of the frequency-dominated block, consisting of the DWTFormer block and IDWTFormer block, is particularly noteworthy. By incorporating a spatial features aggregation scheme (SFAS) to the Swin Transformer and combining it with the wavelet transform, the authors have effectively tackled the limitation of the global receptive field of the Swin Transformer when dealing with complex dusty backgrounds. This combination allows for more accurate perception and removal of dust from agricultural images.

Furthermore, the cross-level information fusion module proposed in DedustNet enables the fusion of different levels of features, resulting in a more comprehensive understanding of global and long-range feature relationships. This module is crucial for capturing contextual information and enhancing the ability to accurately dedust images in varying agricultural environments.

The use of a dilated convolution module guided by wavelet transform at multiple scales is another important aspect of DedustNet. This module leverages the advantages of both wavelet transform and dilated convolution to capture contextual information effectively. By incorporating contextual information at different scales, DedustNet can better infer the structural and textural features of an image while removing dust.

In terms of performance, DedustNet demonstrates superior results compared to existing state-of-the-art methods for agricultural image dedusting. This showcases its potential for practical application in real-world dusty environments. Additionally, the generalization ability of DedustNet is impressive, as it performs well not only on hazy datasets but also in application tests related to computer vision.

In conclusion, DedustNet presents a well-designed and effective solution for removing dust from agricultural images. Its combination of the Swin Transformer, wavelet transform, spatial features aggregation scheme, cross-level information fusion module, and dilated convolution module allows for accurate dedusting while preserving the original structural and textural features. I anticipate that further research and improvement on this approach will continue to enhance the performance and reliability of automated agricultural machines in dusty environments.

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