Data-efficient and accurate rapeseed leaf area estimation by self-supervised vision transformer for germplasms early evaluation.

Abstract

Early-stage, accurate and high-throughput phenotyping‌ through leaf area estimation is ‌critical‌ for future rapeseed breeding, but faces ‌two key constraints‌: expensive data annotation and persistent challenge of leaf occlusion. To address these issues, we present a ‌data-efficient‌ deep learning framework using smartphone-captured top-down RGB images for rapeseed leaf area quantification. Our approach utilizes a two-stage strategy where a Vision Transformer (ViT) backbone is first pre-trained on a large, aggregated dataset of diverse, non-rapeseed public plant datasets using the DINOv2 self-supervised learning method. This pre-trained model is then fine-tuned on a custom rapeseed dataset using a novel Canopy-Mix data augmentation technique to handle fragmented views analogous to occlusion, and a hybrid loss function combining Smooth L1 and Log-Cosh for robust convergence. Through rigorous 5-fold cross-validation, our proposed model achieved strong predictive performance (Coefficient of Determination, R[Formula: see text]=0.805). Moreover, the predicted leaf area demonstrated a remarkably strong correlation with both fresh weight (r=0.900) and dry weight (r=0.885). The model significantly outperformed a range of baselines, including models trained from scratch, those pre-trained on ImageNet, and a heuristic method based on manually annotated bounding boxes. Ablation studies confirmed the essential contribution of each component, while qualitative analysis of attention maps demonstrated the model's ability to precisely localize the leaf canopy and ignore background distractors. This study demonstrates that domain-specific self-supervised pre-training offers a powerful solution to overcome data limitations in agricultural vision, providing a robust and scalable tool for non-destructive phenotyping that can potentially accelerate the rapeseed breeding cycle.

Plant phenotyping relevance

葉面積という植物形質をRGB画像から推定する深層学習法を開発し、交差検証・ベースライン比較・アブレーションで技術検証しているため、方法が中心である。

Code and data availability

The paper's rapeseed image dataset and analysis code are not publicly released (authors state 'No datasets were generated or analysed during the current study' and provide no code/data availability URL). The public plant datasets used for pre-training (CVPPP, Flavia, Plant Pathology 2021, Plant Seedlings, VegAnn) are C

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