一种基于田间表型特征的统计方法,用于计算大豆育种场的高通量出现率,基于田间表型特征
Yan Sun1, Mengqi Li1, Meiling Liu1
1Shenyang Agricultural University, Shenyang, China.
Plant methods
|March 24, 2025
概括
这项研究引入了一种先进的方法,用于使用无人机 (UAV) 数据和深度学习准确计算大豆苗. 这种创新方法显著提高了大豆在智能育种计划中出现率统计的效率和精度.
科学领域:
- 农业科学 农业科学
- 植物育种 植物育种
- 遥感 遥感 遥感 遥感
背景情况:
- 准确的大豆生长率统计对于智能育种至关重要,但目前的方法在低吞吐量,效率和精度方面扎,特别是在密集的种植环境中.
- 环境因素和苗木重叠使传统的计数方法变得复杂,需要高通量表型的创新解决方案.
研究的目的:
- 开发和验证一种有效和精确的统计方法,用于用无人机 (UAV) 尺度和地面测量数据计数大豆苗.
- 在密集种植条件下提高大豆育种选的吞吐量,效率和准确性.
主要方法:
- 使用对比度增强过,超绿色固有值和Otsu算法进行的组合背景细分方法用于图像预处理.
- 深度学习对象检测模型 (特别是Yolov8n) 用于大豆幼苗的识别和标签.
- 开发了新的算法,包括"生长正常化"和"种子间封闭计数算法",并采用了软策略来限制盒子重叠,以准确量化种子.
主要成果:
- 拟议的方法在计数大豆幼苗方面实现了99.18%的整体准确性,错误率为0.82%.
- 对于幼苗检测,Yolov8n表现出强的性能,平均平均精度 (mAP) 为85.15%.
- 背景细分技术提高了4.06%的mAP,突出了其提高检测准确性的有效性.
结论:
- 开发的基于无人机的方法为在密集种植场景中对大豆出现率的统计分析提供了强有力的支持,大大加快了育种过程.
- 这种创新方法提供了新的见解,并为开发植物育种中高效选技术提供了有价值的参考.
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