Automatic estimation of trunk cross sectional area using deep learning

This paper presents an automated method for estimating the trunk cross sectional area of fruit trees. An Intel RealSense 435i was used to capture RGB images and point clouds of individual trunks. To segment the trunk in the image from the background, a Masked-attention Mask Transformer model was adopted. The segmentation results were integrated with the 3D point cloud to estimate trunk widths in 3D. The width estimation was evaluated on three diverse datasets collected from a commercial apple orchard using human measurements as ground truth. With a mean absolute error less than 5%, the method is sufficiently accurate to assist orchard operations.