Ontology-driven dual-channel relational graph convolutional network (DCR-GCN) for lettuce leaf phenotype classification.

Abstract

Lettuce (Lactuca sativa L.) is an important leafy vegetable with substantial diversity in leaf topology, geometry, color, and texture, which poses challenges for germplasm identification and new variety protection. However, current approaches to complex phenotypic analysis are often limited in their ability to explicitly represent and exploit semantic relationships among phenotypic traits. To address this limitation, a knowledge graph-enhanced graph learning framework for lettuce phenotypic traits was developed. Phenotypic traits were first extracted from leaf images of five lettuce types. Based on the trait description standards of the International Union for the Protection of New Varieties of Plants (UPOV), a Lettuce Leaf Phenotypic Trait Knowledge Graph (LLPT-KG) was constructed to represent semantic associations among traits. On this basis, a Dual-Channel Relational Graph Convolutional Network (DCR-GCN) was developed to jointly integrate node attribute features and graph structural information for lettuce type classification. To improve interpretability, node- and edge-level importance analyses were further performed to identify the phenotypic traits and semantic relations most relevant to type discrimination. The proposed framework achieved an accuracy of 0.94 and a Macro-F1 score of 0.94. Compared with the best-performing single-channel graph baseline, R-GCN (Relational Graph Convolutional Network), DCR-GCN improved accuracy by approximately 9% points and Macro-F1 by 10% points. These results demonstrate that combining knowledge graphs with graph neural networks can effectively capture complex phenotypic relationships in lettuce and improve classification performance. The proposed framework provides methodological support for precise lettuce germplasm identification, digital phenotypic evaluation for new variety protection, and digital-assisted pre-screening prior to field-based DUS (Distinctness, Uniformity, and Stability) testing.

Plant phenotyping relevance

レタス葉画像から形態・色・テクスチャ等の表現型形質を抽出し、知識グラフとGNNによる分類手法を開発・評価しており、表現型取得・解析が研究の中心である。

Code and data availability

The supplied blocks describe lettuce leaf image collection, phenotypic trait extraction, the LLPT-KG knowledge graph, and the DCR-GCN model, but contain no data availability statement, no public dataset or image deposit, and no author code/model release with a URL. The only URLs present are the article DOI, the license

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