Yu Kang, Kirchgessner Norbert, Grieder Christoph, Walter Achim, Hund Andreas
Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092 Zurich, Switzerland.
Agroscope, Reckenholzstrasse 191, 8046 Zurich, Switzerland.
Plant Methods. 2017 Mar 21;13:15. doi: 10.1186/s13007-017-0168-4. eCollection 2017.
Robust segmentation of canopy cover (CC) from large amounts of images taken under different illumination/light conditions in the field is essential for high throughput field phenotyping (HTFP). We attempted to address this challenge by evaluating different vegetation indices and segmentation methods for analyzing images taken at varying illuminations throughout the early growth phase of wheat in the field. 40,000 images taken on 350 wheat genotypes in two consecutive years were assessed for this purpose.
We proposed an image analysis pipeline that allowed for image segmentation using automated thresholding and machine learning based classification methods and for global quality control of the resulting CC time series. This pipeline enabled accurate classification of imaging light conditions into two illumination scenarios, i.e. high light-contrast (HLC) and low light-contrast (LLC), in a series of continuously collected images by employing a support vector machine (SVM) model. Accordingly, the scenario-specific pixel-based classification models employing decision tree and SVM algorithms were able to outperform the automated thresholding methods, as well as improved the segmentation accuracy compared to general models that did not discriminate illumination differences.
The three-band vegetation difference index (NDI3) was enhanced for segmentation by incorporating the HSV-V and the CIE Lab-a color components, i.e. the product images NDI3V and NDI3a. Field illumination scenarios can be successfully identified by the proposed image analysis pipeline, and the illumination-specific image segmentation can improve the quantification of CC development. The integrated image analysis pipeline proposed in this study provides great potential for automatically delivering robust data in HTFP.
从田间在不同光照/光线条件下拍摄的大量图像中准确分割冠层覆盖度（CC）对于高通量田间表型分析（HTFP）至关重要。我们试图通过评估不同的植被指数和分割方法来应对这一挑战，以分析在田间小麦生长早期不同光照条件下拍摄的图像。为此，我们评估了连续两年在(350)个小麦基因型上拍摄的(40000)张图像。
我们提出了一种图像分析流程，该流程允许使用自动阈值处理和基于机器学习的分类方法进行图像分割，并对所得的CC时间序列进行全局质量控制。通过使用支持向量机（SVM）模型，该流程能够将一系列连续采集图像中的成像光照条件准确分类为两种光照场景，即高光照对比度（HLC）和低光照对比度（LLC）。因此，采用决策树和SVM算法的特定场景像素分类模型优于自动阈值处理方法，并且与未区分光照差异的通用模型相比，提高了分割精度。
通过合并HSV-V和CIE Lab-a颜色分量（即乘积图像NDI3V和NDI3a），增强了三波段植被差异指数（NDI3）用于分割的能力。所提出的图像分析流程能够成功识别田间光照场景，并且特定光照条件下的图像分割可以改善CC发育的量化。本研究中提出的集成图像分析流程在HTFP中自动提供可靠数据方面具有巨大潜力。
