XooNet:一种基于无人机的高通量方法,用于野外查野生大米中耐菌病菌的生殖质
Pan Pan1,2,3, Wenlong Guo4,5, Mingxia Li1,2,3
1National Nanfan Research Institute, Chinese Academy of Agricultural Sciences, Sanya, China.
Frontiers in plant science
|March 9, 2026
概括
一种新的基于无人机的方法,XooNet,有效地选野生大米的细菌病 (BB) 耐药性. 这种自动化系统有助于发现有价值的基因资源,用于改进作物.
科学领域:
- 农业科学 农业科学
- 植物病理学 植物病理学
- 遥感 遥感 遥感 遥感
背景情况:
- 细菌病 (BB) 显著影响米生产,需要有效的方法来识别耐药野生米生殖质,用于育种计划.
- 传统的BB选方法耗时且主观,而目前基于无人机 (UAV) 的技术通常是昂贵的或缺乏全面的解决方案.
研究的目的:
- 开发和验证XooNet,一种基于无人机的新型自动化系统,用于高通量查野生大米中的细菌病菌耐药性.
- 创建一个高精度,轻量级的定向界限框 (OBB) 检测算法,专门用于识别野生大米图像中的BB.
主要方法:
- 实施基于无人机的系统,用于在野生大米中进行自动化细菌病耐药性分类.
- 开发一种专门的OBB检测算法,用于精确地定位BB症状.
- 应用LAMP修剪策略,以优化检测模型的效率和减少计算负载.
主要成果:
- 自动选方法在分类野生大米耐药性水平方面取得了97.5%的高精度.
- 优化的检测模型,LAMP后修剪,保持了93.1%的准确性,参数大小减少 (1.4M) 和计算复杂性减少 (3.5 GFLOPs).
结论:
- XooNet提供了一种高效,自动化和具有成本效益的解决方案,用于选野生大米生殖质,以检测细菌病菌耐药性.
- 这种方法可以加速识别有价值的遗传资源,支持开发更有弹性的米品种.
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