Citrus Pest Identification Model Based on Improved ShuffleNet
| Autor: | Yan-Nan Yu, Chun-Lin Xiong, Ji-Chi Yan, Yong-Bin Mo, Shi-Qing Dou, Zuo-Hua Wu, Rong-Feng Yang |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Applied Sciences, Vol 14, Iss 11, p 4437 (2024) |
| Druh dokumentu: | article |
| ISSN: | 14114437 2076-3417 |
| DOI: | 10.3390/app14114437 |
| Popis: | To address the current issues of complex structures and low accuracies in citrus pest identification models, a lightweight pest identification model was proposed. First, a parameterized linear rectification function was introduced to avoid neuronal death. Second, the model’s attention to pest characteristics was improved by incorporating an improved mixed attention mechanism. Subsequently, the network structure of the original model was adjusted to reduce architectural complexity. Finally, by employing transfer learning, an SCHNet model was developed. The experimental results indicated that the proposed model achieved an accuracy rate of 94.48% with a compact size of 3.84 MB. Compared to the original ShuffleNet V2 network, the SCHNet model showed a 3.12% accuracy improvement while reducing the model size by 22.7%. The SCHNet model exhibited an excellent classification performance for citrus pest identification, enabling the accurate identification of citrus pests. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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