A hyperspectral deep learning attention model for predicting lettuce chlorophyll content.

Abstract

Background The phenotypic traits of leaves are the direct reflection of the agronomic traits in the growth process of leafy vegetables, which plays a vital role in the selection of high-quality leafy vegetable varieties. The current image-based phenotypic traits extraction research mainly focuses on the morphological and structural traits of plants or leaves, and there are few studies on the phenotypes of physiological traits of leaves. The current research has developed a deep learning model aimed at predicting the total chlorophyll of greenhouse lettuce directly from the full spectrum of hyperspectral images. Results A CNN-based one-dimensional deep learning model with spectral attention module was utilized for the estimate of the total chlorophyll of greenhouse lettuce from the full spectrum of hyperspectral images. Experimental results demonstrate that the deep neural network with spectral attention module outperformed the existing standard approaches, including partial least squares regression (PLSR) and random forest (RF), with an average R 2 of 0.746 and an average RMSE of 2.018. Conclusions This study unveils the capability of leveraging deep attention networks and hyperspectral imaging for estimating lettuce chlorophyll levels. This approach offers a convenient, non-destructive, and effective estimation method for the automatic monitoring and production management of leafy vegetables.

Plant phenotyping relevance

ハイパースペクトル画像からレタス葉のクロロフィル量という生理形質を推定する深層学習手法を開発・比較評価しており、表現型取得が研究の中心である。

Code and data availability

The paper's hyperspectral lettuce dataset and chlorophyll measurements are not publicly deposited; the authors state they are available only on reasonable request. No public code, model checkpoints, or data URLs are provided.

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