Analyzing different phenotypic methods of soybean leaves under the high temperature stress with near-infrared spectroscopy, microscopic Image, and multispectral image

Abstract

High temperature stress (HT) plays an important role in soybean selection and breeding, it can cause changes in soybean physiological, biochemical and morphological traits, and directly affect the growth and yield of soybean plants. Among these changes, soybean leaves are particularly sensitive to HT during growth and development. It is important to establish a non-destructive method to distinguish the phenotypic differences between soybean plants under HT and control (CK). In this study, data from two years of soybean field trials were used. In the first year, phenotypic information was collected by near-infrared spectroscopy (NIR), microscopic images, and further difference analysis and classification modelling experiments were conducted. In the second year, multispectral image data were collected and analyzed by Soybean high temperature mask autoencoder (SHT_MAE). The SHT_MAE model with a 75% masking ratio achieved an accuracy of 89.16% and an F1-score of 89.18%. Compared with one-dimensional near-infrared and two-dimensional microscopic image fusion models, the classification accuracy of HT and CK is improved by 2.68%. The accuracy of SHT_MAE multispectral model was improved by 16.84% and 6.88%, respectively, compared with models using only NIR or microscopic images. Both spectral and imaging methods effectively distinguish the phenotypic differences between HT and CK soybean leaves, with the multispectral approach based on the SHT_MAE model demonstrating a clear advantage. This study realized the effective distinction of soybean leaves under HT and CK. It provides theoretical support for HT intelligent breeding (using artificial intelligence and data analysis to optimize breeding decisions) and high temperature grade prediction.

Plant phenotyping relevance

高温ストレス下のダイズ葉の表現型差を、近赤外分光・顕微鏡画像・マルチスペクトル画像と分類モデルで非破壊的に抽出・比較する手法研究であり、表現型取得と解析が中心である。

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