MOS sensor array based on multi-modal data weighted composite membership optimization for rice blast detection in symptomless stage

Abstract

Rice blast is one of the most destructive diseases affecting rice crops globally, known for its rapid spread and significant damage. The initial phenotypic symptoms are challenging to identify, and conventional field detection methods frequently do not fulfill the necessary detection requirements. We have developed an innovative detection method for the symptomless stage of rice blast disease, utilizing metal oxide semiconductor (MOS) gas sensors in conjunction with multimodal data optimization techniques. The distinctive fingerprint characteristics of volatile organic compounds (VOCs) emitted by rice were systematically collected. Principal Component Analysis (PCA) was utilized to identify the relevant feature sensors. A significant correlation was observed between the responses of the sensors and the physiological indicators of rice leaves, including chlorophyll and hydrogen peroxide concentrations, with a correlation coefficient of r = -0.91. The criteria importance through intercriteria correlation (CRITIC) weighting method, along with the gaussian membership function (GMF), has been employed to analyze and integrate sensor feature data, thereby effectively differentiating the characteristics associated with the symptomless stage of rice blast. The processed features were utilized to train, calibrate, and validate a model based on Support Vector Machine (SVM) technology, culminating in the development of the CRITIC-GMF-SVM framework for the precise detection of rice blast during its symptomless stage. This model achieved an impressive accuracy rate of 98.37 % for the symptomless stage, significantly outperforming conventional on-site detection techniques. This method offers significant advantages in the early detection and precise monitoring of rice blast disease. Moreover, its low cost and high stability enable a wide range of applications, making it well-suited for complex field environments.

Plant phenotyping relevance

イネの無症状病害という植物状態をMOSガスセンサーとデータ解析で検出する方法を開発し、学習・較正・検証しているため、植物フェノタイピング手法が中心である。

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