论文标题
使用快速地面真相的植物茎分段
Plant Stem Segmentation Using Fast Ground Truth Generation
论文作者
论文摘要
准确的表型植物枯萎对于理解对环境压力的反应很重要。分析植物的形状可能可用于准确量化枯萎程度。可以通过定位茎来增强植物形状分析，该茎是枯萎过程中一致的参考点。在本文中，我们表明深度学习方法可以准确地分割番茄植物茎。我们还提出了一种基于控制点的基础真实方法，该方法大大减少了为深度学习方法创建培训数据集所需的资源。实验结果表明，我们提出的地面真理方法和基于深度学习的STEM分割的生存能力。
Accurately phenotyping plant wilting is important for understanding responses to environmental stress. Analysis of the shape of plants can potentially be used to accurately quantify the degree of wilting. Plant shape analysis can be enhanced by locating the stem, which serves as a consistent reference point during wilting. In this paper, we show that deep learning methods can accurately segment tomato plant stems. We also propose a control-point-based ground truth method that drastically reduces the resources needed to create a training dataset for a deep learning approach. Experimental results show the viability of both our proposed ground truth approach and deep learning based stem segmentation.
