Image processing for high-throughput phenotyping of seeds

Abstract

High-throughput phenotyping of seeds is the assessment of seed morphometry to aid in the prediction of yield, tolerance, resistance, and development of seeds in various environmental conditions. The paper focuses on the application of 3D graphics to image processing as a means to conduct seed phenotyping better. The paper proposes two algorithms-similar in the outcome, but different in implementation. The algorithms perform image processing on a variety of seeds such as wheat, soy, sorghum, rough rice, white rice, and canola to arrive at their morphometric estimations. In the area of static image processing, addressed are at least three common yet significant problems of seed clusters on images, skewed images, and poor image quality. As a means to address the problems, we propose the use of low-cost physical components. The algorithms provide the estimated count, area, perimeter, length, and width of seeds within an image.