Large-Scale and High-Accuracy Phenotyping of Populus simonii Leaves Using the Colony Counter and OpenCV

Abstract

Image-based morphometric technology is broadly applicable to generate large-scale phenomic datasets in ecological, genetic and morphological studies. However, little is known about the performance of image-based measuring methods on plant morphological characters. In this study, we presented an automatic image-based workflow to obtain the accurate estimations for basic leaf characteristics (e.g., ratio of length/width, length, width, and area) from a hundred Populus simonii pictures, which were captured on Colony counter Scan1200. The image-based workflow was implemented with Python and OpenCV, and subdivided into three parts, including image pre-processing, image segmentation and object contour detection. Six image segmentation methods, including Chan-Vese, Iterative threshold, K-Mean, Mean, OSTU, and Watershed, differed in the running time, noise sensitivity and accuracy. The image-based estimates and measured values for leaf morphological traits had a strong correlation coefficient (r2 > 0.9736), and their residual errors followed a Gaussian distribution with a mean of almost zero. Iterative threshold, K-Mean, OSTU, and Watershed overperformed the other two methods in terms of efficiency and accuracy. This study highlights the high-quality and high-throughput of autonomous image-based phenotyping and offers a guiding clue for the practical use of suitable image-based technologies in biological and ecological research.