التفاصيل البيبلوغرافية
| العنوان: |
An Improved Multi-Scale Feature Extraction Network for Rice Disease and Pest Recognition. |
| المؤلفون: |
Lv, Pengtao, Xu, Heliang, Zhang, Yana, Zhang, Qinghui, Pan, Quan, Qin, Yao, Chen, Youyang, Cao, Dengke, Wang, Jingping, Zhang, Mengya, Chen, Cong |
| المصدر: |
Insects (2075-4450); Nov2024, Vol. 15 Issue 11, p827, 25p |
| مصطلحات موضوعية: |
RICE diseases & pests, IMAGE recognition (Computer vision), FOOD security, FEATURE extraction, AGRICULTURE |
| مستخلص: |
Simple Summary: Rice is one of the most important sources of food for humans. However, rice production is frequently threatened by pests and diseases, resulting in significant losses. In this study, we developed a model that can assist agricultural practitioners in accurately identifying different types of rice pests and diseases. Our model can accurately identify seven different categories of rice pests and diseases, which enables agriculturalists to promptly identify the causes of crop damage and take appropriate measures to protect their crops. We hope that the application of this technology will reduce global rice losses and alleviate the problem of the global food crisis. In the process of rice production, rice pests are one of the main factors that cause rice yield reduction. To implement prevention and control measures, it is necessary to accurately identify the types of rice pests and diseases. However, the application of image recognition technologies focused on the agricultural field, especially in the field of rice disease and pest identification, is relatively limited. Existing research on rice diseases and pests has problems such as single data types, low data volume, and low recognition accuracy. Therefore, we constructed the rice pest and disease dataset (RPDD), which was expanded through data enhancement methods. Then, based on the ResNet structure and the convolutional attention mechanism module, we proposed a Lightweight Multi-scale Feature Extraction Network (LMN) to extract multi-scale features at a finer granularity. The proposed LMN model achieved an average classification accuracy of 95.38% and an F1-Score of 94.5% on the RPDD. The parameter size of the model is 1.4 M, and the FLOPs is 1.65 G. The results suggest that the LMN model performs rice disease and pest classification tasks more effectively than the baseline ResNet model by significantly reducing the model size and improving accuracy. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
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