SFPGRN: spectral detection method of plant diseases based on deep learning

Yi Zhou; Zeng Pan; Zuhao Liu; Tingwei He; Jingwen Yan

doi:10.1117/12.2684756

21 July 2023 SFPGRN: spectral detection method of plant diseases based on deep learning

Yi Zhou, Zeng Pan, Zuhao Liu, Tingwei He, Jingwen Yan

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127171F (2023) https://doi.org/10.1117/12.2684756
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

At present, most of the spectral detection methods with plant diseases are based on machine learning, we present a novel approach to spectral analysis using a spectral feature pseudo-graph-based residual network, referred to as SFPGRN, for data in the near-infrared band. The proposed method involves constructing a residual network model using a characteristic surface obtained via natural neighborhood interpolation based on preprocessed near-infrared spectral reflection signal and first-order differential spectral index. The input layer is a pseudo spectral map that is generated through surface projection of the characteristic surface. The generated spectral pseudo image contains a diverse set of spectral features that augment the representation capability of the pseudo-image while retaining the spectral band reflectance information. We evaluated the proposed method on the 2015 spectral dataset of apple leaf diseases and insect pests in China and found that it outperforms traditional machine learning algorithms, achieving a classification accuracy of 93.21%.

Citation Download Citation

Yi Zhou, Zeng Pan, Zuhao Liu, Tingwei He, and Jingwen Yan "SFPGRN: spectral detection method of plant diseases based on deep learning", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127171F (21 July 2023); https://doi.org/10.1117/12.2684756

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