Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Classification of maize leaf diseases based on hyperspectral imaging technology

Not Accessible

Your library or personal account may give you access

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

PDF Article
More Like This
Rational selection of RGB channels for disease classification based on IPPG technology

Ge Xu, Liquan Dong, Jing Yuan, Yuejin Zhao, Ming Liu, Mei Hui, Yuebin Zhao, and Lingqin Kong
Biomed. Opt. Express 13(4) 1820-1833 (2022)

Tumor tissue classification based on micro-hyperspectral technology and deep learning

Bingliang Hu, Jian Du, Zhoufeng Zhang, and Quan Wang
Biomed. Opt. Express 10(12) 6370-6389 (2019)

Tongue fissure extraction and classification using hyperspectral imaging technology

Qingli Li, Yiting Wang, Hongying Liu, Zhen Sun, and Zhi Liu
Appl. Opt. 49(11) 2006-2013 (2010)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved