College of Resources and Environment, Jilin Agricultural University, Changchun, China.
Beijing Research Center for Information Technology in Agriculture, Beijing, China.
PLoS One. 2021 Jan 12;16(1):e0241528. doi: 10.1371/journal.pone.0241528. eCollection 2021.
Image processing technologies are available for high-throughput acquisition and analysis of phenotypes for crop populations, which is of great significance for crop growth monitoring, evaluation of seedling condition, and cultivation management. However, existing methods rely on empirical segmentation thresholds, thus can have insufficient accuracy of extracted phenotypes. Taking maize as an example crop, we propose a phenotype extraction approach from top-view images at the seedling stage. An end-to-end segmentation network, named PlantU-net, which uses a small amount of training data, was explored to realize automatic segmentation of top-view images of a maize population at the seedling stage. Morphological and color related phenotypes were automatic extracted, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle. The results show that the approach can segment the shoots at the seedling stage from top-view images, obtained either from the UAV or tractor-based high-throughput phenotyping platform. The average segmentation accuracy, recall rate, and F1 score are 0.96, 0.98, and 0.97, respectively. The extracted phenotypes, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle, are highly correlated with manual measurements (R2 = 0.96-0.99). This approach requires less training data and thus has better expansibility. It provides practical means for high-throughput phenotyping analysis of early growth stage crop populations.
图像处理技术可用于高通量获取和分析作物群体的表型，这对于作物生长监测、幼苗状况评估和栽培管理具有重要意义。然而，现有的方法依赖于经验分割阈值，因此提取的表型可能不够准确。以玉米为例，我们提出了一种从幼苗期顶视图图像中提取表型的方法。我们探索了一种端到端的分割网络，称为 PlantU-net，它可以使用少量的训练数据来实现对玉米群体幼苗期顶视图图像的自动分割。自动提取了与形态和颜色相关的表型，包括玉米苗的覆盖度、外接圆半径、纵横比和植株方位平面角。结果表明，该方法可以从无人机或拖拉机高通量表型平台获得的顶视图图像中分割出幼苗期的茎。平均分割精度、召回率和 F1 分数分别为 0.96、0.98 和 0.97。提取的表型，包括玉米苗的覆盖度、外接圆半径、纵横比和植株方位平面角，与手动测量高度相关（R2=0.96-0.99）。该方法需要较少的训练数据，因此具有更好的可扩展性。它为早期生长阶段作物群体的高通量表型分析提供了实用手段。
