  Phenotyping is the process of measuring an organism's observable traits.
Manual phenotyping of crops is a labor-intensive, time-consuming, costly, and
error prone process. Accurate, automated, high-throughput phenotyping can
relieve a huge burden in the crop breeding pipeline. In this paper, we propose
a scalable, high-throughput approach to automatically count and segment
panicles (heads), a key phenotype, from aerial sorghum crop imagery. Our
counting approach uses the image density map obtained from dot or region
annotation as the target with a novel deep convolutional neural network
architecture. We also propose a novel instance segmentation algorithm using the
estimated density map, to identify the individual panicles in the presence of
occlusion. With real Sorghum aerial images, we obtain a mean absolute error
(MAE) of 1.06 for counting which is better than using well-known crowd counting
approaches such as CCNN, MCNN and CSRNet models. The instance segmentation
model also produces respectable results which will be ultimately useful in
reducing the manual annotation workload for future data.
