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A hyperspectral deep learning attention model for predicting lettuce chlorophyll content

文献类型： 外文期刊

作者： Ye, Ziran 1 ; Tan, Xiangfeng 1 ; Dai, Mengdi 1 ; Chen, Xuting 1 ; Zhong, Yuanxiang 1 ; Zhang, Yi 2 ; Ruan, Yunjie 3 ; Kong, Dedong 1 ;

作者机构： 1.Zhejiang Acad Agr Sci, Inst Digital Agr, Hangzhou 310021, Zhejiang, Peoples R China

2.UCAS, Hangzhou Inst Adv Study, Hangzhou 310024, Peoples R China

3.Zhejiang Univ, Coll Biosyst Engn & Food Sci, lnst Agr Bioenvironm Engn, Hangzhou 310058, Peoples R China

4.Zhejiang Univ, Acad Rural Dev, Hangzhou 310058, Peoples R China

关键词： Lettuce; Chlorophyll content; Beep learning; Hyperspectral

期刊名称：PLANT METHODS （ 影响因子：4.7； 五年影响因子：5.6 ）

ISSN：

年卷期： 2024 年 20 卷 1 期

页码：

收录情况： SCI

摘要： BackgroundThe 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.ResultsA 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 R2 of 0.746 and an average RMSE of 2.018.ConclusionsThis 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.

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