Precision silviculture: use of UAVs and comparison of deep learning models for the identification and segmentation of tree crowns in pine crops

Abstract

The monitoring of trees is crucial for the management of large areas of forest cultivations, but this process may be costly. However, remotely sensed data offers a solution to automate this process. In this work, we used two neural network methods named You Only Look Once (YOLO) and Mask R-CNN to overcome the challenging tasks of counting, detecting, and segmenting high dimensional Red–Green–Blue (RGB) images taken from unmanned aerial vehicles (UAVs). We present a processing framework, which is suitable to generate accurate predictions for the aforementioned tasks using a reasonable amount of labeled data. We compared our method using forest stands of different ages and densities. For counting, YOLO overestimates 8.5% of the detected trees on average, whereas Mask R-CNN overestimates a 4.7% of the trees. For the detection task, YOLO obtains a precision of 0.72 and a recall of 0.68 on average, while Mask R-CNN obtains a precision of 0.82 and a recall of 0.80. In segmentation, YOLO overestimates a 13.5% of the predicted area on average, whereas Mask R-CNN overestimates a 9.2%. The proposed methods present a cost-effective solution for forest monitoring using RGB images and have been successfully used to monitor $\sim 146\comma\; 500$ acres of pine cultivations.

Keywords

• artificial intelligence
• tree identification
• forestry
• remote sensing