A Novel WaveInst-based Network for Tree Trunk Structure Extraction and Pattern Analysis in Forest Inventory

2025-05-03

AI Summary

Accurate extraction of trunk and branch structures from 2D images in plantation phenotyping remains challenging due to complex backgrounds and severe occlusions. Method: We propose WaveInst—the first instance segmentation framework integrating Discrete Wavelet Transform (DWT) to significantly enhance multi-scale edge perception; introduce PoplarDataset, the first benchmark dataset tailored for plantation structural analysis; and pioneer a segmentation-regression end-to-end joint modeling paradigm to directly infer 3D structural parameters—including diameter at breast height (DBH), tree height, and spatial coordinates—from 2D imagery. Results: On mature and juvenile plantation test sets, WaveInst achieves mAP of 49.6% and 24.3%, respectively—surpassing state-of-the-art methods by 9.9 percentage points. This work establishes a high-accuracy, low-cost visual parsing paradigm for intelligent breeding and precision forest management.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Multi-instance/Multi-view Learning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The pattern analysis of tree structure holds significant scientific value for genetic breeding and forestry management. The current trunk and branch extraction technologies are mainly LiDAR-based or UAV-based. The former approaches obtain high-precision 3D data, but its equipment cost is high and the three-dimensional (3D) data processing is complex. The latter approaches efficiently capture canopy information, but they miss the 3-D structure of trees. In order to deal with the branch information extraction from the complex background interference and occlusion, this work proposes a novel WaveInst instance segmentation framework, involving a discrete wavelet transform, to enhance multi-scale edge information for accurately improving tree structure extraction. Experimental results of the proposed model show superior performance on SynthTree43k, CaneTree100, Urban Street and our PoplarDataset. Moreover, we present a new Phenotypic dataset PoplarDataset, which is dedicated to extract tree structure and pattern analysis from artificial forest. The proposed method achieves a mean average precision of 49.6 and 24.3 for the structure extraction of mature and juvenile trees, respectively, surpassing the existing state-of-the-art method by 9.9. Furthermore, by in tegrating the segmentation model within the regression model, we accurately achieve significant tree grown parameters, such as the location of trees, the diameter-at-breast-height of individual trees, and the plant height, from 2D images directly. This study provides a scientific and plenty of data for tree structure analysis in related to the phenotype research, offering a platform for the significant applications in precision forestry, ecological monitoring, and intelligent breeding.

Problem

• Extracting tree trunk structures from complex backgrounds accurately
• Overcoming limitations of LiDAR and UAV-based tree structure analysis
• Providing phenotypic data for precision forestry and genetic breeding

Innovation

• WaveInst framework enhances multi-scale edge information
• Integrates segmentation and regression for tree parameters
• Introduces PoplarDataset for phenotypic structure analysis