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Outline
Aiming to address the issues of small target size, varying pest postures, and low detection efficiency, and suboptimal performance of trapping devices during live rice pest identification, an intelligent recognition method was proposed based on an improved YOLO v8n model integrated with a one-to-three live targeted trapping device. By optimizing the structural design of the traditional targeted recognition equipment, a one-to-three live targeted trapping device was developed to enhance operational efficiency. For the construction of the dataset required for intelligent recognition, a frame-by-frame image clarity comparison and sorting method was proposed to improve data quality. Additionally, an adaptive module was introduced into the improved CBAM attention mechanism to enhance the model's focus on target regions, and a high-resolution P2 layer was added to the AFPN structure to improve small target recognition capability. Comparative experiments demonstrated that the improved YOLO v8n model achieved a mean average precision (mAP@0.5) of 95.65% on the rice leaf folder dataset. Ablation experiments further verified that the enhanced CBAM module and the introduction of the P2 layer in the AFPN structure significantly improved model performance, resulting in optimal overall recognition capability. Field trials confirmed the practical effectiveness of the improved YOLO v8n model, achieving a recognition rate of over 90% for rice leaf folder pests across 60 mu (approximately 4 hectares) of rice fields when combined with the targeted identification device, demonstrating good robustness and stability. In summary, the intelligent recognition method for live rice pests based on YOLO v8n the improved model proposed effectively enhanced recognition accuracy and efficiency, providing a scientific basis for precise pest identification and control in rice fields, with strong practical application value and promotion potential.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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