Nondestructive determination of glucoraphanin in broccoli by deep learning and phenotypic fusion

Abstract

The accurate quantification of glucoraphanin (GRA), a crucial health-promoting compound in broccoli, is vital for assessing its nutritional quality. However, traditional methods relying on destructive laboratory assays hinder rapid quality monitoring. To address this limitation, we developed a novel non-destructive, multimodal deep learning framework that integrates two phenotypic data modalities—image-based phenotypes from red-green-blue (RGB) leaf images and field-measured plant morphological traits—for accurate GRA estimation. Our proposed model, Parallel-Enhanced FasterNet (PE-FasterNet), incorporates two key innovations: a Gated Parallel Routing Attention (GPRA) mechanism for enhanced feature extraction, and a Phenotype-Guided Cross-Attention Feature Fusion (PG-CAFF) module for effective cross-modal fusion. Through rigorous evaluation, the model achieved a standard random-split test R 2 of 0.985 and a Leave-One-Group-Out (LOGO) cross-validation R 2 of 0.979, demonstrating highly accurate and generalized GRA predictions. This performance represents a substantial improvement over state-of-the-art convolutional neural network (CNN) and Vision Transformer models, affirming the architectural superiority of our approach. This study not only provides a robust tool for rapid, non-destructive prediction of GRA but also demonstrates a viable pathway toward data-driven crop quality management and precision breeding in broccoli.

Plant phenotyping relevance

ブロッコリー葉画像と形態形質からグルコラファニンを非破壊推定する深層学習法を開発・検証しており、表現型取得・抽出ワークフローが研究の中心である。

Code and data availability

The supplied blocks describe a paper-specific multimodal broccoli dataset (600 RGB leaf images, 12 morphological traits, HPLC GRA labels) and the PE-FasterNet model, but contain no data availability statement, repository deposit, or authors' public URL for the dataset, images, code, or trained model. No qualifying, cit

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