Automatic Counting of Planting Microsites via Local Visual Detection and Global Count Estimation

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.