Agricultural Pest Super-Resolution and Identification With Attention Enhanced Residual and Dense Fusion Generative and Adversarial Network
The growth of the most significant field crops such as rice, wheat, maize, and soybean are influenced because of various pests. And crop production is decreased due to various categories of insects. Deep learning technologies significantly increased the efficiency of identifying and controlling agri...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9082695/ |
| _version_ | 1819276484839211008 |
|---|---|
| author | Qiang Dai Xi Cheng Yan Qiao Youhua Zhang |
| author_facet | Qiang Dai Xi Cheng Yan Qiao Youhua Zhang |
| author_sort | Qiang Dai |
| collection | DOAJ |
| description | The growth of the most significant field crops such as rice, wheat, maize, and soybean are influenced because of various pests. And crop production is decreased due to various categories of insects. Deep learning technologies significantly increased the efficiency of identifying and controlling agricultural pests attack. However, agricultural pests images obtained are often obscure and unclear because of the sparse density of cameras deployed in the real farmland. This always makes pests difficult to recognize and monitor. Additionally, the existing classification and segmentation methods are not satisfying for the identification of low-resolution images because they are pre-trained on the clear and high-resolution datasets. Therefore, it is crucial to restore and upscale the obtained low-resolution pest images in order to improve classification accuracy and the recall rate of the instance segmentation. In this paper, we propose a generative adversarial network (GAN) with quadra-attention and residual and dense fusion mechanisms to transform low-resolution pest images. Compared with previous state-of-the-art PSNR-oriented super-resolution methods, our proposed method is more powerful in image reconstruction and achieves the state of the art performance. The experiment results show that after reconstructing with our proposed gan, the recall rate increased by 182.89% and classification accuracy also improved a lot. Besides, our proposed method could decrease the density of the camera layout in the agricultural Internet of Things (IOT) monitor systems and the cost of infrastructure, which is practical for real-world applications. |
| first_indexed | 2024-12-23T23:40:58Z |
| format | Article |
| id | doaj.art-0aaabb1832a54c9fb64e8f4f5ce2f04c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-23T23:40:58Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0aaabb1832a54c9fb64e8f4f5ce2f04c2022-12-21T17:25:40ZengIEEEIEEE Access2169-35362020-01-018819438195910.1109/ACCESS.2020.29915529082695Agricultural Pest Super-Resolution and Identification With Attention Enhanced Residual and Dense Fusion Generative and Adversarial NetworkQiang Dai0https://orcid.org/0000-0002-8942-834XXi Cheng1https://orcid.org/0000-0001-7479-7575Yan Qiao2https://orcid.org/0000-0002-4407-1762Youhua Zhang3https://orcid.org/0000-0003-1519-4509School of Information and Computer, Anhui Agricultural University, Hefei, ChinaSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, ChinaSchool of Information and Computer, Anhui Agricultural University, Hefei, ChinaSchool of Information and Computer, Anhui Agricultural University, Hefei, ChinaThe growth of the most significant field crops such as rice, wheat, maize, and soybean are influenced because of various pests. And crop production is decreased due to various categories of insects. Deep learning technologies significantly increased the efficiency of identifying and controlling agricultural pests attack. However, agricultural pests images obtained are often obscure and unclear because of the sparse density of cameras deployed in the real farmland. This always makes pests difficult to recognize and monitor. Additionally, the existing classification and segmentation methods are not satisfying for the identification of low-resolution images because they are pre-trained on the clear and high-resolution datasets. Therefore, it is crucial to restore and upscale the obtained low-resolution pest images in order to improve classification accuracy and the recall rate of the instance segmentation. In this paper, we propose a generative adversarial network (GAN) with quadra-attention and residual and dense fusion mechanisms to transform low-resolution pest images. Compared with previous state-of-the-art PSNR-oriented super-resolution methods, our proposed method is more powerful in image reconstruction and achieves the state of the art performance. The experiment results show that after reconstructing with our proposed gan, the recall rate increased by 182.89% and classification accuracy also improved a lot. Besides, our proposed method could decrease the density of the camera layout in the agricultural Internet of Things (IOT) monitor systems and the cost of infrastructure, which is practical for real-world applications.https://ieeexplore.ieee.org/document/9082695/Agricultural pestssuper-resolutionclassificationobject instance segmentationdeep learningquadra-attention |
| spellingShingle | Qiang Dai Xi Cheng Yan Qiao Youhua Zhang Agricultural Pest Super-Resolution and Identification With Attention Enhanced Residual and Dense Fusion Generative and Adversarial Network IEEE Access Agricultural pests super-resolution classification object instance segmentation deep learning quadra-attention |
| title | Agricultural Pest Super-Resolution and Identification With Attention Enhanced Residual and Dense Fusion Generative and Adversarial Network |
| title_full | Agricultural Pest Super-Resolution and Identification With Attention Enhanced Residual and Dense Fusion Generative and Adversarial Network |
| title_fullStr | Agricultural Pest Super-Resolution and Identification With Attention Enhanced Residual and Dense Fusion Generative and Adversarial Network |
| title_full_unstemmed | Agricultural Pest Super-Resolution and Identification With Attention Enhanced Residual and Dense Fusion Generative and Adversarial Network |
| title_short | Agricultural Pest Super-Resolution and Identification With Attention Enhanced Residual and Dense Fusion Generative and Adversarial Network |
| title_sort | agricultural pest super resolution and identification with attention enhanced residual and dense fusion generative and adversarial network |
| topic | Agricultural pests super-resolution classification object instance segmentation deep learning quadra-attention |
| url | https://ieeexplore.ieee.org/document/9082695/ |
| work_keys_str_mv | AT qiangdai agriculturalpestsuperresolutionandidentificationwithattentionenhancedresidualanddensefusiongenerativeandadversarialnetwork AT xicheng agriculturalpestsuperresolutionandidentificationwithattentionenhancedresidualanddensefusiongenerativeandadversarialnetwork AT yanqiao agriculturalpestsuperresolutionandidentificationwithattentionenhancedresidualanddensefusiongenerativeandadversarialnetwork AT youhuazhang agriculturalpestsuperresolutionandidentificationwithattentionenhancedresidualanddensefusiongenerativeandadversarialnetwork |