MRSliceNet: Multi-Scale Recursive Slice and Context Fusion Network for Instance Segmentation of Leaves from Plant Point Clouds

TL;DR

MRSliceNet is a new deep learning framework that improves leaf segmentation in plant point clouds for better plant phenotyping.

Contribution

The novel MRSliceNet framework introduces a multi-scale recursive slicing module and context fusion for accurate leaf instance segmentation.

Findings

MRSliceNet achieves state-of-the-art performance with AP scores of 55.04% and 53.78% on two datasets.

The framework successfully handles challenges like occlusion and uneven point density in plant point clouds.

It provides clear leaf boundaries and reliable instance identification in real-world agricultural settings.

Abstract

Plant phenotyping plays a vital role in connecting genotype to environmental adaptability, with important applications in crop breeding and precision agriculture. Traditional leaf measurement methods are laborious and destructive, while modern 3D sensing technologies like LiDAR provide high-resolution point clouds but face challenges in automatic leaf segmentation due to occlusion, geometric similarity, and uneven point density. To address these challenges, we propose MRSliceNet, an end-to-end deep learning framework inspired by human visual cognition. The network integrates three key components: a Multi-scale Recursive Slicing Module (MRSM) for detailed local feature extraction, a Context Fusion Module (CFM) that combines local and global features through attention mechanisms, and an Instance-Aware Clustering Head (IACH) that generates discriminative embeddings for precise instance separation. Extensive experiments on two challenging datasets show that our method establishes new state-of-the-art performance, achieving AP of 55.04%/53.78%, AP50 of 65.37%/64.00%, and AP25 of 74.68%/73.45% on Dataset A and Dataset B, respectively. The proposed framework not only produces clear boundaries and reliable instance identification but also provides an effective solution for automated plant phenotyping, as evidenced by its successful implementation in real-world agricultural research pipelines.

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