Abstract
Curvularia lunata and Aureobasidium zeae are the main leaf diseases of maize in Northeast China. These two diseases are similar and difficult to distinguish. In order to diagnose diseases correctly, a diagnostic method based on hyperspectral imaging technology for Curvularia lunata and Aureobasidium zeae was proposed. The experimental leaves were inoculated in vivo, and a hyperspectral imaging system was used to collect hyperspectral image data of leaves with Curvularia lunata, leaves with Aureobasidium zeae, and normal leaves in the near-infrared band. By analyzing the spectral characteristics of chlorotic spots and normal leaves, it is found that there is a significant difference in the spectral information between chlorotic spots and normal leaves. Then, by means of confidence interval estimation and significance testing, the characteristic bands of Curvularia lunata and Aureobasidium zeae can be distinguished. Finally, the classification model of the support vector machine is established based on the characteristic bands. The results show that the 10 characteristic bands of 412.7, 416.3, 421.2, 465.1, 484.8, 580.9, 615, 640.5, 676.2, and 880.8 nm can be used to distinguish between the spectral characteristics of Curvularia lunata and Aureobasidium zeae , and the support vector machine classification model established by the above band is used for 288 samples. The accuracy rate was 96.7%. These results provide a theoretical basis and technical method for rapid and nondestructive diagnosis of Curvularia lunata and Aureobasidium zeae.
© 2020 Optical Society of America
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