Automatic counting of planting microsites via local visual detection and
  global count estimation
Ahmed Zgaren Wassim Bouachir Nizar Bouguila
In forest industry, mechanical site preparation by mounding is widely used
prior to planting operations. One of the main problems when planning planting
operations is the difficulty in estimating the number of mounds present on a
planting block, as their number may greatly vary depending on site
characteristics. This estimation is often carried out through field surveys by
several forestry workers. However, this procedure is prone to error and
slowness. Motivated by recent advances in UAV imagery and artificial
intelligence, we propose a fully automated framework to estimate the number of
mounds on a planting block. Using computer vision and machine learning, we
formulate the counting task as a supervised learning problem using two
prediction models. A local detection model is firstly used to detect visible
mounds based on deep features, while a global prediction function is
subsequently applied to provide a final estimation based on block-level
features. To evaluate the proposed method, we constructed a challenging UAV
dataset representing several plantation blocks with different characteristics.
The performed experiments demonstrated the robustness of the proposed method,
which outperforms manual methods in precision, while significantly reducing
time and cost.