Hyperspectral imaging and dynamic selective peak transformer for early-stage classification of lettuce heat responses.

Abstract

Introduction Early-stage detection and classification of lettuce heat responses are essential for non-destructive phenotyping, yet conventional assessment mainly relies on visible symptoms and manual observation. Methods This study constructed a lettuce hyperspectral dataset comprising heat-sensitive and heat-tolerant varieties under control and high-temperature treatments, and proposed the Dynamic Selective Peak Transformer (DSPformer). DSPformer integrates edge-enhanced feature extraction, dynamic multi-scale spatial-spectral representation, Peak-k selective attention, and a confusion-aware dynamic focal loss to enhance discriminative features while reducing spectral redundancy, class imbalance, and inter-class confusion. Results Under the patch-level evaluation protocol, DSPformer achieved 96.22% accuracy, 95.55% recall, 96.35% precision, and 95.95% F1-score, outperforming the compared CNN- and Transformer-based models. Day-wise evaluation showed that DSPformer reached 82.61% accuracy on Day 1 and 96.55% on Day 3, before visible heat-stress symptoms appeared on Day 6. Under a plant-level partition protocol, DSPformer maintained robust performance with 93.76 +/- 0.49% accuracy. Additional evaluation on the Indian Pines benchmark further demonstrated the applicability of DSPformer to general hyperspectral image classification. Discussion These findings suggest that hyperspectral imaging can capture heat-stress-sensitive information beyond visual phenotypes, and that DSPformer provides a promising framework for early, non-destructive lettuce heat-response screening and hyperspectral phenotyping-assisted breeding.

Plant phenotyping relevance

レタスの熱ストレス応答を非破壊的に早期分類するため、ハイパースペクトル画像データセットと新規Transformer手法を開発・評価しており、植物表現型取得・抽出が中心である。

Code and data availability

The paper describes a self-constructed SAGC lettuce hyperspectral dataset (2,646 patches) and the DSPformer model, but the supplied blocks contain no data availability statement, repository deposit, or authors' public URL for the dataset, images, or code. The Indian Pines benchmark is a generic public dataset, not a论文-

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