  The future landscape of modern farming and plant breeding is rapidly changing
due to the complex needs of our society. The explosion of collectable data has
started a revolution in agriculture to the point where innovation must occur.
To a commercial organization, the accurate and efficient collection of
information is necessary to ensure that optimal decisions are made at key
points of the breeding cycle. However, due to the shear size of a breeding
program and current resource limitations, the ability to collect precise data
on individual plants is not possible. In particular, efficient phenotyping of
crops to record its color, shape, chemical properties, disease susceptibility,
etc. is severely limited due to labor requirements and, oftentimes, expert
domain knowledge. In this paper, we propose a deep learning based approach,
named DeepStand, for image-based corn stand counting at early phenological
stages. The proposed method adopts a truncated VGG-16 network as a backbone
feature extractor and merges multiple feature maps with different scales to
make the network robust against scale variation. Our extensive computational
experiments suggest that our proposed method can successfully count corn stands
and out-perform other state-of-the-art methods. It is the goal of our work to
be used by the larger agricultural community as a way to enable high-throughput
phenotyping without the use of extensive time and labor requirements.
