An image analysis pipeline for automated classification of imaging light conditions and for quantification of wheat canopy cover time series in field phenotyping.

ABSTRACT:

Background

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.

Results

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 classif

SUBMITTER: Yu K 

PROVIDER: S-EPMC5361853 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature