Detection of Strigolactone-Treated wheat seeds via Dual-View hyperspectral data fusion and deep learning

文献类型： 外文期刊

第一作者： Gu, Ying

作者： Gu, Ying;Chen, Liping;Gu, Ying;Feng, Guoqing;Zhang, Han;Hou, Peichen;Wang, Cheng;Chen, Liping;Luo, Bin

作者机构：

关键词： Hyperspectral imaging; Data fusion; Deep learning; Strigolactones; Convolutional neural network

期刊名称：MICROCHEMICAL JOURNAL （ 影响因子：5.1； 五年影响因子：4.7 ）

ISSN： 0026-265X

年卷期： 2025 年 215 卷

页码：

收录情况： SCI

摘要： Strigolactones (SLs) play a crucial role in regulating plant growth and development. However, the soaking concentration significantly affects the growth of wheat seeds. Currently, there is a lack of rapid and accurate detection methods for this purpose. The objective of this study is to develop a rapid and accurate detection method for wheat seeds treated with varying concentrations of SLs, utilizing dual-view hyperspectral data fusion combined with deep learning techniques. Wheat seeds were soaked in different SLs concentrations, and hyper-spectral data were collected from both the embryo and endosperm surface. The spectral data were preprocessed using a combination of Savitzky-Golay (SG), Second Derivative (ddA), and Multiplicative Scatter Correction (MSC). To effectively utilize the spectral data from both sides of the seeds, Parallel, Concat, and Stack data fusion strategies were employed. Detection was performed using Self-Built Convolutional Neural Network (SCNN), Adaptive Boosting (AdaBoost), and Gradient Boosting Decision Tree (GBDT) models. Results showed that the SG-MSC preprocessing combination demonstrated the best performance across all models. Compared to single-view spectral data, dual-view data improved the detection performance of the models. Furthermore, the Stack fusion strategy effectively avoided information redundancy and loss when processing dual-view data, outperforming both Concat and Parallel fusion strategies. The SCNN-SG-MSC-Stack model is the optimal model, achieving Accuracy, Precision, Recall, and F1-score values of 99.26%, 99.27%, 99.26%, and 0.99, respectively. This study demonstrates that combining dual-view hyperspectral data fusion and deep learning provides an efficient and reliable method for detecting different SLs concentrations in seed soaking, offering new insights for rapid evaluation.

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