Tree Morphology for Phenotyping from Semantics-Based Mapping in Orchard Environments

TL;DR

This paper introduces a vision-based system using RGB-D cameras to accurately measure tree morphology in orchards, offering a low-cost alternative to expensive LIDAR-based methods.

Contribution

A novel semantics-based mapping approach that merges 3D models from both sides of orchard rows using only RGB-D data, improving accuracy and reducing equipment costs.

Findings

Accurately measures tree height, canopy volume, and trunk diameter.

Outperforms traditional LIDAR-based methods in orchard environments.

Demonstrates robustness and precision in real-world experiments.

Abstract

Measuring tree morphology for phenotyping is an essential but labor-intensive activity in horticulture. Researchers often rely on manual measurements which may not be accurate for example when measuring tree volume. Recent approaches on automating the measurement process rely on LIDAR measurements coupled with high-accuracy GPS. Usually each side of a row is reconstructed independently and then merged using GPS information. Such approaches have two disadvantages: (1) they rely on specialized and expensive equipment, and (2) since the reconstruction process does not simultaneously use information from both sides, side reconstructions may not be accurate. We also show that standard loop closure methods do not necessarily align tree trunks well. In this paper, we present a novel vision system that employs only an RGB-D camera to estimate morphological parameters. A semantics-based mapping algorithm merges the two-sides 3D models of tree rows, where integrated semantic information is obtained and refined by robust fitting algorithms. We focus on measuring tree height, canopy volume and trunk diameter from the optimized 3D model. Experiments conducted in real orchards quantitatively demonstrate the accuracy of our method.