Pest Identification Based on Fusion of Self-Attention With ResNet
Pest identification is a challenging task in the agricultural sector, as accurate and timely detection of pests is essential for effective pest control and crop protection. Conventional approaches to pest detection, such as entomological knowledge and manual examination, take a lot of time and are p...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10382486/ |
| _version_ | 1797356394795499520 |
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| author | Sk Mahmudul Hassan Arnab Kumar Maji |
| author_facet | Sk Mahmudul Hassan Arnab Kumar Maji |
| author_sort | Sk Mahmudul Hassan |
| collection | DOAJ |
| description | Pest identification is a challenging task in the agricultural sector, as accurate and timely detection of pests is essential for effective pest control and crop protection. Conventional approaches to pest detection, such as entomological knowledge and manual examination, take a lot of time and are prone to human mistakes. The advent of Deep Learning (DL) techniques has revolutionized the field of computer vision, enabling automated and efficient pest recognition systems.In this research, we compared the effectiveness of many deep learning models and suggested an enhanced approach for more effective feature extraction. In the proposed approach, we have incorporated two parallel attention mechanisms in ResNet architectures and it has a significant improvement in performance. Experimental result shows that the performance accuracy obtained in ResNet50-SA, ResNet101-SA, and ResNet152-SA is 99.80%, 88.48% and 96.68%, respectively. The performance of ResNet50-SA outperforms the other state of art deep learning by a large margin. The result shows that ResNet with self-attention (SA) has a better ability to extract features and focus on the important features which increases the performance. |
| first_indexed | 2024-03-08T14:26:58Z |
| format | Article |
| id | doaj.art-41f54fa60baa4b22b8ebd41b27252d63 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T14:26:58Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-41f54fa60baa4b22b8ebd41b27252d632024-01-13T00:01:31ZengIEEEIEEE Access2169-35362024-01-01126036605010.1109/ACCESS.2024.335100310382486Pest Identification Based on Fusion of Self-Attention With ResNetSk Mahmudul Hassan0https://orcid.org/0000-0002-3714-9453Arnab Kumar Maji1https://orcid.org/0000-0002-3320-9965School of Computer Science and Engineering, VIT-AP University, Amravati, IndiaDepartment of Information Technology, School of Technology, NEHU, Shillong, IndiaPest identification is a challenging task in the agricultural sector, as accurate and timely detection of pests is essential for effective pest control and crop protection. Conventional approaches to pest detection, such as entomological knowledge and manual examination, take a lot of time and are prone to human mistakes. The advent of Deep Learning (DL) techniques has revolutionized the field of computer vision, enabling automated and efficient pest recognition systems.In this research, we compared the effectiveness of many deep learning models and suggested an enhanced approach for more effective feature extraction. In the proposed approach, we have incorporated two parallel attention mechanisms in ResNet architectures and it has a significant improvement in performance. Experimental result shows that the performance accuracy obtained in ResNet50-SA, ResNet101-SA, and ResNet152-SA is 99.80%, 88.48% and 96.68%, respectively. The performance of ResNet50-SA outperforms the other state of art deep learning by a large margin. The result shows that ResNet with self-attention (SA) has a better ability to extract features and focus on the important features which increases the performance.https://ieeexplore.ieee.org/document/10382486/Pest identificationdeep learningresidual networkself attention |
| spellingShingle | Sk Mahmudul Hassan Arnab Kumar Maji Pest Identification Based on Fusion of Self-Attention With ResNet IEEE Access Pest identification deep learning residual network self attention |
| title | Pest Identification Based on Fusion of Self-Attention With ResNet |
| title_full | Pest Identification Based on Fusion of Self-Attention With ResNet |
| title_fullStr | Pest Identification Based on Fusion of Self-Attention With ResNet |
| title_full_unstemmed | Pest Identification Based on Fusion of Self-Attention With ResNet |
| title_short | Pest Identification Based on Fusion of Self-Attention With ResNet |
| title_sort | pest identification based on fusion of self attention with resnet |
| topic | Pest identification deep learning residual network self attention |
| url | https://ieeexplore.ieee.org/document/10382486/ |
| work_keys_str_mv | AT skmahmudulhassan pestidentificationbasedonfusionofselfattentionwithresnet AT arnabkumarmaji pestidentificationbasedonfusionofselfattentionwithresnet |