Abstract
We explore a metric-learning approach to create representations of
sorghum images grown in a field setting. We train a convolutional neural
network to embed images so that images from the same variety have
similar features and images of different varieties have different
features. We that these features are good at discriminating unseen
cultivars, can be used to predict standard phenotypes (height and leaf
length and width), and can be used to predict presence or absence of
genetic mutations. We evaluate these results using TERRA-REF data from
field-scale trials of hundreds of varieties of sorghum. This
demonstrates an end-to-end solution for creating useful image phenotypes
in