Cone Detection of Abies sachalinensis Using a Convolutional Neural Network with Unmanned Aerial Vehicle (UAV) Images

Abstract

An object detection model for detecting the cones of Abies sachalinensis on the tree crown using images shot by unmanned aerial vehicles (UAV) was developed. We used the image recognition algorithm "You Only Look Once (YOLO) v4" based on a convolutional neural network and examined its accuracy. Training was performed using 356 pictures with 6,138 cones, and the constructed model was adapted to 92 validation pictures with 1,692 cones. As a result, an average precision (AP) of 88.5％ was obtained. However, small white round objects were often detected as cones (false positives) and densely situated cones were not detected (false negative). Improvement of those misdetections will be future subject. We conclude that cone detection of A. sachalinensis using YOLOv4 is possible, and the model will be useful to confirm cone producing individuals in seed orchards.