@article {10.3844/ajbbsp.2023.384.393,
article_type = {journal},
title = {A New Disease Index Based on Multi-Spectra of UAV to Estimate Cotton Disease},
author = {Chen, Bing and Wang, Jing and Wang, Qiong and Liu, Taijie and Yu, Yu and Song, Yong and Chen, Zijie and Bai, Zhikun},
volume = {19},
number = {4},
year = {2024},
month = {Mar},
pages = {384-393},
doi = {10.3844/ajbbsp.2023.384.393},
url = {https://thescipub.com/abstract/ajbbsp.2023.384.393},
abstract = {Verticillium wilt is a significant disease that affects cotton plants, which can lead to stunted growth and reduced yield. To address this, a multi-spectral comprehensive monitoring disease index model is developed using an Unmanned Aerial Vehicle (UAV) to monitor the severity of cotton Verticillium wilt. First, multi-spectral dates were collected from Hexacopter (HY-6X) and the phenotype disease grade of cotton plants at monitoring sites was investigated. Then, a new indicator for cotton diseases was established using the correlation coefficient method and optimal index factor method and the regression models for four types of cotton diseases were established. The results show that cotton plants with different severity of Verticillium wilt have different spectral characteristics in the near-infrared and visible light bands. As the disease severity increased, the spectral reflectance of the cotton canopy increased from 470-656nm. Combined Difference Vegetation Index (DVI) with B3-B5-B8, a new index, UAV multispectral comprehensive monitoring disease index is created. Taking the comprehensive indicator as the independent variable, a regression model including multiple-linear regression, partial least squares regression, principal component analysis and support vector machine regression is established. The results show the support vector machine regression model has the highest accuracy (prediction set R2 = 0.91, RMSE = 0.07; validation set R2 = 0.89, RMSE = 0.08; and the linear relationship is significant at the 95% level). Compared with other indicators, using UAV for monitoring cotton disease severity will be the optimal model for motoring the severity of cotton diseases.},
journal = {American Journal of Biochemistry and Biotechnology},
publisher = {Science Publications}
}