基于机器视觉的检测方法,以检测石竹茎的关键表型信息
Jiuxiao Zhao1,2, Mingfei Wang1,2, Zuolin Li2,3
1Intelligent Equipment Technology Research Center,Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Frontiers in plant science
|February 9, 2026
概括
通过先进的图像处理,现在可以准确地测量石竹的形状特征. 这一突破提高了shiitake的育种效率和作物质量.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 生物技术是生物技术.
背景情况:
- 测量关键的shiitake的形状特征是具有挑战性和不准确的.
- 这种限制阻碍了在西竹种植中的育种效率和产品质量.
研究的目的:
- 开发一种准确和有效的方法来检测石竹的关键特征.
- 为了解决当前石竹繁殖实践的局限性.
主要方法:
- 开发了一个粘度检测模型 (ACmix-ADown-YOLOv11n) 和使用EfficientSAM.N的精确细分.
- 使用OpenCV计算12个表型特征,以及用于中线预测的CoTAttention-YOLOv11n-Ghost-pose算法.
主要成果:
- 检测模型实现了93.7%的AP,姿势算法实现了97.2%的AP,回忆率为96.1%.
- 观察到高准确度 (R2 > 0.978) 预测跨类别的长度.
结论:
- 拟议的图像处理方法是有效和可靠的,用于检测shiitake的形特征.
- 这项技术显著支持智能石竹繁殖,并提高了种植质量.
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