Seed shape and size are among the most important
agronomic traits because they affect yield, eating quality, and market price. Therefore, plant research fields
such as genetics, functional analysis, and genomicsassisted crop improvement, in addition to breeding
programs, could benefit from quantitative evaluation
of seed shape. Efficient, reliable, high-throughput
phenotyping methods are required.
In general, seed shape can be scored in two ways.
The simple way is to measure seed length (L) and
width (W) with calipers. However, manual methods
have limits to the number of data, the quality of
measurements, and the variety of shape data that can
be gleaned. By contrast, computational methods using
digital imaging technology could enable us to automatically measure a variety of shape parameters at
very small sizes in high-resolution images (Brewer
et al., 2006; Bylesjö et al., 2008; Weight et al., 2008;