Crop Leaf Disease Image Super-Resolution and Identification with Dual Attention and Topology Fusion Generative Adversarial Network

Qiang Dai, Xi Cheng, Yan Qiao, Youhua Zhang
2020 IEEE Access  
For agricultural disease image identification, obtained images are typically unclear, which can lead to poor identification results in real production environments. The quality of an image has a significant impact on the identification accuracy of pre-trained image classifiers. To address this problem, we propose a generative adversarial network with dual-attention and topology-fusion mechanisms called DATFGAN. This network can effectively transform unclear images into clear and high-resolution
more » ... images. Additionally, the weight sharing scheme in our proposed network can significantly reduce the number of parameters. Experimental results demonstrate that DATFGAN yields more visually pleasing results than state-of-the-art methods. Additionally, treated images are evaluated based on identification tasks. The results demonstrate that the proposed method significantly outperforms other methods and is sufficiently robust for practical use. INDEX TERMS Crop leaf disease, attention, generative adversarial networks, super-resolution, identification. 55724 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see VOLUME 8, 2020
doi:10.1109/access.2020.2982055 fatcat:byx6nmhyb5hx3jj6vqwj4kvjtm