对松病的四层预警框架:整合物种分布模型和智能传感方法
Haojie Bi1, Xiaoyu Xin1, Wenlu Liu1
1State Key Laboratory to Efficient Production of Forest Resources, Beijing Forestry University, Beijing, China.
Pest management science
|October 20, 2025
概括
使用人工智能和智能传感的新四层框架为松病 (PWD) 提供了早期警告. 该系统提高了检测准确度,降低了森林健康监测的成本,有助于疾病控制.
科学领域:
- 林业林业 林业 林业 林业
- 植物病理学 植物病理学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 松病 (PWD) 是由松木线虫 (PWN) 引起的,是针叶树林的主要威胁.
- 有效的预警系统对于PWD管理至关重要,但受到多种环境压力因素的挑战.
- 开发了一个新的四层框架,提供常规级别的PWD风险警报,整合先进的技术.
研究的目的:
- 开发和验证一个用于常规水平的松病早期预警的操作框架.
- 通过尽量减少其他森林压力因素的干扰,提高PWD检测准确度.
- 减少与森林健康监测相关的劳动力和财务成本.
主要方法:
- 综合物种分布建模 (MaxEnt) 与智能传感 (无人机,物联网,人工智能).
- 开发了PWN和昆虫载体分布,宿主树种和健康状况检测的模型.
- 实施了四级警报系统 (蓝色,黄色,色,红色),表明感染风险增加.
主要成果:
- 马克森特模型在松树和载体预测方面实现了AUC>0.92.
- 主体树和昆虫载体检测模型显示高精度 (mAP0.5分别为97.5%和99.8%).
- 该框架准确地确定了16个已确认的PWD感染的树,其中有34棵变色的树木,全部来自红色预警区.
结论:
- 这四层框架提供了一个可行和准确的解决方案,用于常态级的PWD早期预警.
- 边境技术的整合提高了森林健康监测的可扩展性和成本效益.
- 该系统为高风险森林地区的PWD管理提供了一个创新的工具.
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