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DEKR-SPrior:一个高效的自下而上的关键点检测模型,用于精确的豆表型化

Jingjing He¹, Lin Weng¹, Xiaogang Xu²

¹Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China.

Plant phenomics (Washington, D.C.)

|June 28, 2024

概括

精确计算大豆豆和种子是具有挑战性的,因为密集的包装. 一个新的深度学习模型,DEKR-SPrior,通过使用结构前模块和子图像分析来提高现场表型准确性.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
植物生物学植物生物学

背景情况:

豆和种子数量是决定产量的关键特征.
这些特征的高通量,准确的表型是育种者面临的重大挑战.
现有的深度学习模型在密集和重叠的大豆中扎.

研究的目的:

开发一个准确和高效的深度学习模型,用于现场大豆和种子的表型.
解决当前模型在处理密集和重叠的作物结构方面的局限性.
引入一种新的高通量植物表型化方法.

主要方法:

提出了一个自下而上的深度学习模型,命名为DEKR-SPrior (与结构先验解关键点回归).
开发了一种新的结构先验 (SPrior) 模块,使用等号相似性来改进特征歧视.
采用全尺寸图像的剪切成更小,高分辨率的子图像进行详细分析.

主要成果:

与Lightweight-OpenPose,OpenPose,HigherHRNet和DEKR等现有模型相比,DEKR-SPrior表现出优异的性能.
该模型将表型中的平均绝对误差从25.81 (原DEKR) 降低到21.11.
在区分和计数靠近的大豆种子方面取得了更高的准确性.

结论:

DEKR-SPrior为准确的现场大豆和种子表型定型提供了一个有前途的解决方案.
开发的模型在推进高通量植物表型化方面具有显著的潜力.
这种方法可以帮助种植者更有效地选择优质的大豆品种.

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