  Flowering time (time to flower after planting) is important for estimating
plant development and grain yield for many crops including sorghum. Flowering
time of sorghum can be approximated by counting the number of panicles
(clusters of grains on a branch) across multiple dates. Traditional manual
methods for panicle counting are time-consuming and tedious. In this paper, we
propose a method for estimating flowering time and rapidly counting panicles
using RGB images acquired by an Unmanned Aerial Vehicle (UAV). We evaluate
three different deep neural network structures for panicle counting and
location. Experimental results demonstrate that our method is able to
accurately detect panicles and estimate sorghum flowering time.
