Early detection of cotton Verticillium wilt based on generative adversarial networks and hyperspectral imaging technology

Abstract

Cotton is one of most important economic crops in the world. Cotton yield has been significantly affected by frequent infestations of Verticillium wilt (VW). Currently, most detection methods for cotton VW are implemented based on the clearly visible symptoms, leading to delayed interventions and control. Therefore, early detection of cotton VW is crucial for minimizing economic losses. However, existing early detection methods for cotton VW face substantial challenges due to the subtle nature of early-stage symptoms and the limited availability of data, which result in considerable error. To address this, an early detection method for cotton VW by integrating Generative Adversarial Networks (GANs) with hyperspectral imaging technology was proposed, focusing primarily on cotton hyperspectral data augmentation. GANS-based spectral enhancement model (Spe-GAN) and GANS-based spatial-enhancement model (Spa-GAN) were developed to capture subtle early-stage symptoms of VW under limited data from both spectral and spatial perspectives. Compared to traditional machine learning methods (RF and SVM) and deep learning methods (LSTM and ResNet18), the proposed Spe-GAN and Spa-GAN achieved better detection performance, with accuracy rates of 94.52 % and 91.78 %, respectively. Moreover, this study also explored the underlying reasons for the superiority of the proposed method from various perspectives, further enhancing the model interpretability. The data augmentation method proposed in this study provided a new perspective and opened up possibilities for achieving the early detection of cotton VW and other plant diseases.

Plant phenotyping relevance

綿花の病害状態（Verticillium wilt）を、ハイパースペクトル画像とGANによって早期推定する方法を開発・比較評価しており、植物フェノタイピング手法が研究の中心である。

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