UAV cotton flower counting dataset

Many perennial plants make important contributions to agroeconomies and
agroecosystems, but have complex architecture and/or long flowering
duration that hinders measurement and selection. Iteratively tracking
productivity over a long flowering/fruiting season may permit the
identification of genetic factors conferring different reproductive
strategies that might be successful in different environments, ranging
from rapid early maturation that avoids stresses, to late maturation that
utilizes the full seasonal duration to maximize productivity. In cotton, a
perennial plant that is generally cultivated as an annual crop, we apply
aerial imagery and deep learning methods to novel and stable genetic
stocks, identifying genetic factors influencing the duration and rate of
fruiting. While these factors may have different relationships with crop
productivity and quality in different environments, their determination
adds potentially important information to breeding decisions. With
transfer learning of the deep learning models, this approach could be
applied widely, potentially improving gains from selection in diverse
perennial shrubs and trees essential to sustainable agricultural
intensification.