Reverse temporal spectral signal extraction and deep spectral signal decoupling fusion for detection of SCLB infection process

Abstract

Close-range spectral imaging provides technical support for leaf detection during the early infection process of SCLB (southern corn leaf blight). However, due to the randomness of pathogen infection and the low visibility of early lesions, the temporal spectral signals obtained using this technique have poor continuity and low sensitivity. To improve the ability of temporal spectral signals to detect early-stage infection, this study proposes a signal extraction method based on reverse temporal spectral image matching, and a signal decoupling method based on DSO-CWT (decomposition of scale-optimized continuous wavelet transform) is also proposed to improve signal sensitivity. First, the spectral images were preprocessed. Image entropy was used to quantify the changing patterns of symptoms in early SCLB infection. Second, a temporal spectral signal extraction method based on ASpanFormer (adaptive span transformer) reverse temporal spectral image matching is proposed. The calculation results of LPIPS (learned perceptual image patch similarity) indicates that the average matching error between adjacent periods is less than 0.2, which suggests that this method can enhance the extraction accuracy of weak temporal spectral signals in early infection. Third, the T-test was used to evaluate the detection sensitivity of temporal spectral signals at different early stages of infection. The results showed that the temporal spectral signal still had low sensitivity for detecting different stages of infection. Therefore, a temporal signal decoupling method based on DSO-CWT is proposed, which enhances the detection sensitivity of temporal spectral signals by performing time–frequency domain conversion. Finally, a diagnostic model for the early SCLB infection was established by fusing fluorescence and reflectance spectral signals. After DSO-CWT processing, the accuracy of the modelling set improved from 47.62% to 94.22%, and the accuracy of the validation set was improved from 47.62% to 91.27%. This study improves the extraction accuracy and detection sensitivity of temporal spectral signals for early SCLB infection by using signal extraction based on reverse temporal spectral image matching and deep signal decoupling based on DSO-CWT, providing new insights for early detection of SCLB infection.

Plant phenotyping relevance

植物病害の感染状態を対象に、時系列スペクトル信号の抽出・分離手法を開発し、検出感度と診断精度を検証しているため、病徴状態のフェノタイピング手法が中心です。

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