Automatic estimation of crop disease severity levels based on vegetation index normalization

Authors: ZHAO, HENGQIAN - China University Of Mining And Technology; Yang, Chenghai; GUO, WEI - Henan Agricultural University; ZHANG, LIFU - Chinese Academy Of Sciences; ZHANG, DONGYAN - Anhui Agricultural University

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/30/2020
Publication Date: 7/7/2020
Citation: Zhao, H., Yang, C., Guo, W., Zhang, L., Zhang, D. 2020. Automatic estimation of crop disease severity levels based on vegetation index normalization. Remote Sensing. 12:1930. https://doi.org/10.3390/rs12121930.
DOI: https://doi.org/10.3390/rs12121930

Interpretive Summary: Timely monitoring of crop disease development is very important for precision agriculture applications. Remote sensing-based vegetation indices can be good indicators of crop disease severity, but current methods are primarily dependent on manual ground survey results. In this study, an automated crop disease severity grading method based on normalized vegetation indices was developed and applied to two cotton fields infested with different levels of cotton root rot in south Texas of the United States, where airborne hyperspectral imagery was collected. Spatial and statistical analysis showed that the disease grading results based on six commonly used vegetation indices were in general agreement with previous ground survey results, proving the validity of the disease severity grading method. With the advantages of independence of ground surveys and potential universal applicability, the newly proposed crop disease grading method will be of great significance for crop disease monitoring over large geographical areas.

Technical Abstract: Timely monitoring of crop disease development is very important for precision agriculture applications. Remote sensing-based vegetation indices (VIs) can be good indicators of crop disease severity, but current methods are mainly dependent on manual ground survey results. Based on VI normalization, an automated crop disease severity grading method without the use of ground surveys was proposed in this study. This technique was applied to two cotton fields infested with different levels of cotton root rot in south Texas of the United States, where airborne hyperspectral imagery was collected. Six typical VIs were calculated from the hyperspectral imagery and their histograms indicated that VI normalization could eliminate the influences of variable field conditions and the VI value range variations, allowing potentially a broader scope of application. According to the analysis of the obtained results from the spectral dimension, spatial dimension and descriptive statistics, the disease grading results were in general agreement with previous ground survey results, proving the validity of the disease severity grading method. Although satisfactory results could be achieved from different types of VIs, there is still room for further improvement through the exploration of more VIs. With the advantages of independence of ground surveys and potential universal applicability, the newly proposed crop disease grading method will be of great significance for crop disease monitoring over large geographical areas.