  Reliable seed yield estimation is an indispensable step in plant breeding
programs geared towards cultivar development in major row crops. The objective
of this study is to develop a machine learning (ML) approach adept at soybean
[\textit{Glycine max} L. (Merr.)] pod counting to enable genotype seed yield
rank prediction from in-field video data collected by a ground robot. To meet
this goal, we developed a multi-view image-based yield estimation framework
utilizing deep learning architectures. Plant images captured from different
angles were fused to estimate the yield and subsequently to rank soybean
genotypes for application in breeding decisions. We used data from controlled
imaging environment in field, as well as from plant breeding test plots in
field to demonstrate the efficacy of our framework via comparing performance
with manual pod counting and yield estimation.
  Our results demonstrate the promise of ML models in making breeding decisions
with significant reduction of time and human effort, and opening new breeding
methods avenues to develop cultivars.
