摘要
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∼146,500 acres of pine cultivations.
