Graph-Based Deep Learning for Component Segmentation of Maize Plants

Abstract

In precision agriculture, one of the most important tasks when exploring crop production is identifying individual plant components. There are several attempts to accomplish this task by the use of traditional 2D imaging, 3D reconstructions, and Convolutional Neural Networks (CNN). However, they have several drawbacks when processing 3D data and identifying individual plant components. Therefore, in this work, we propose a novel Deep Learning architecture to detect components of individual plants on Light Detection and Ranging (LiDAR) 3D Point Cloud (PC) data sets. This architecture is based on the concept of Graph Neural Networks (GNN), and feature enhancing with Principal Component Analysis (PCA). For this, each point is taken as a vertex and by the use of a K-Nearest Neighbors (KNN) layer, the edges are established, thus representing the 3D PC data set. Subsequently, Edge-Conv layers are used to further increase the features of each point. Finally, Graph Attention Networks (GAT) are applied to classify visible phenotypic components of the plant, such as the leaf, stem, and soil. This study demonstrates that our graph-based deep learning approach enhances segmentation accuracy for identifying individual plant components, achieving percentages above 80% in the IoU average, thus outperforming other existing models based on point clouds.

Plant phenotyping relevance

LiDAR 3D点群からトウモロコシの葉・茎などの可視形態構成要素を分割・分類するGNN手法の開発であり、植物表現型の取得・抽出が中心です。

Code and data availability

The supplied blocks describe the EdgeGAT model and experiments but contain no public code/model/data deposit for this paper's own analysis. The maize point cloud data appears to derive from Pheno4D, which is cited prior work, not a paper-specific asset. No authors' code URL or availability statement appears.

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